Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method
Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always...
Saved in:
| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 1; pp. 330 - 344 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure. |
|---|---|
| AbstractList | Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure.Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure. Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure. |
| Author | Zhang, Han Wang, Rong Li, Xuelong Nie, Feiping |
| Author_xml | – sequence: 1 givenname: Xuelong orcidid: 0000-0003-2924-946X surname: Li fullname: Li, Xuelong email: xuelong_li@nwpu.edu.cn organization: School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China – sequence: 2 givenname: Han orcidid: 0000-0001-7750-0028 surname: Zhang fullname: Zhang, Han email: zhanghan0805@mail.nwpu.edu.cn organization: School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China – sequence: 3 givenname: Rong orcidid: 0000-0001-9240-6726 surname: Wang fullname: Wang, Rong email: wangrong@nwpu.edu.cn organization: School of Cybersecurity and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China – sequence: 4 givenname: Feiping orcidid: 0000-0002-0871-6519 surname: Nie fullname: Nie, Feiping email: feipingnie@gmail.com organization: School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China |
| BookMark | eNp9kE1v1DAQhi3Uim4LfwAukbhwydYef8Thtl2xpVJXbUU5W5NkQl1lk8V2QPx7XLbi0AOnOcz7zLx6TtnROI3E2DvBl0Lw-vz-drW9WgIHvpRcCKHsK7YAYXhZQw1HbMGFgdJasCfsNMZHzoXSXL5mJxIqza3kC3a3nYfkf3r6VayHOSYKfvz-qVgVX1scsBmowLErbjHgjvKy3ASi4sLvMSSfqLgMuH8oNnP001hsKT1M3Rt23OMQ6e3zPGPfNp_v11_K65vLq_Xqumwl2FR2FW9aXaEyqgKV2_dto2VnkAx1YGWrJTWgsO97DSYvlKEa0Yi61xqhkWfs4-HuPkw_ZorJ7XxsaRhwpGmODpTkxihQJkc_vIg-TnMYczsHhle20pWGnIJDqg1TjIF6tw9-h-G3E9w9CXd_hbsn4e5ZeIbsC6j1CVPWkQL64f_o-wPqiejfr1rkwlbJP75mjP4 |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1109_TGRS_2025_3540269 crossref_primary_10_1109_TIP_2025_3593057 crossref_primary_10_1016_j_patcog_2024_110892 crossref_primary_10_1109_TCSVT_2024_3393148 crossref_primary_10_1007_s13042_021_01432_3 crossref_primary_10_1109_TNNLS_2024_3473618 crossref_primary_10_1109_TKDE_2023_3268215 crossref_primary_10_1016_j_neucom_2024_128266 crossref_primary_10_1016_j_neucom_2025_130615 crossref_primary_10_1109_TETCI_2024_3502459 crossref_primary_10_1109_TGRS_2022_3183096 crossref_primary_10_1109_TGRS_2025_3538632 crossref_primary_10_1016_j_eswa_2025_127943 crossref_primary_10_1109_TPAMI_2023_3298629 crossref_primary_10_1016_j_engappai_2023_106379 crossref_primary_10_1109_TCAD_2023_3345251 crossref_primary_10_1007_s11390_025_3739_2 crossref_primary_10_1016_j_patcog_2024_110308 crossref_primary_10_1109_TKDE_2025_3583718 crossref_primary_10_1109_TPAMI_2025_3580669 crossref_primary_10_1109_TKDE_2023_3333522 crossref_primary_10_1016_j_neucom_2024_128037 crossref_primary_10_1109_TPAMI_2022_3203157 crossref_primary_10_1016_j_ins_2024_120374 crossref_primary_10_1109_TCSVT_2022_3143848 crossref_primary_10_1109_TPAMI_2022_3187976 crossref_primary_10_1109_TCSVT_2023_3312979 crossref_primary_10_3724_SP_J_1089_2022_19522 crossref_primary_10_1109_TKDE_2024_3392209 crossref_primary_10_1109_TCYB_2024_3451292 crossref_primary_10_1109_TNNLS_2025_3552969 crossref_primary_10_1016_j_neucom_2024_127513 crossref_primary_10_1109_TBDATA_2023_3325045 crossref_primary_10_1109_TCYB_2023_3321843 crossref_primary_10_1016_j_landusepol_2025_107474 crossref_primary_10_1109_TPAMI_2022_3179556 crossref_primary_10_1109_TCYB_2025_3535722 crossref_primary_10_1109_TNNLS_2022_3219131 crossref_primary_10_1016_j_eswa_2025_126878 crossref_primary_10_1109_TIP_2022_3141612 crossref_primary_10_1016_j_dsp_2025_105443 crossref_primary_10_1016_j_neucom_2023_127102 crossref_primary_10_1016_j_neucom_2024_127523 crossref_primary_10_3390_e26110992 crossref_primary_10_1109_TNNLS_2023_3256066 crossref_primary_10_1109_TCSS_2021_3109151 crossref_primary_10_1016_j_dsp_2022_103847 crossref_primary_10_1109_TFUZZ_2024_3421576 crossref_primary_10_1016_j_patcog_2022_108809 crossref_primary_10_1109_LSP_2025_3560529 crossref_primary_10_1109_TNNLS_2022_3184970 crossref_primary_10_1109_TPAMI_2022_3217137 crossref_primary_10_1109_TETCI_2024_3375342 crossref_primary_10_1109_TNNLS_2025_3558613 crossref_primary_10_1109_TFUZZ_2025_3580408 crossref_primary_10_1002_cpe_70134 crossref_primary_10_3390_electronics14040817 crossref_primary_10_1016_j_inffus_2024_102393 crossref_primary_10_1016_j_ins_2024_120281 crossref_primary_10_1016_j_asoc_2024_112538 crossref_primary_10_1109_TPAMI_2022_3202158 crossref_primary_10_1016_j_patcog_2024_111140 crossref_primary_10_1109_TKDE_2025_3569681 crossref_primary_10_1016_j_patcog_2025_112195 crossref_primary_10_1109_JSTARS_2025_3538174 crossref_primary_10_1109_TNNLS_2022_3201964 crossref_primary_10_1016_j_patcog_2024_110860 crossref_primary_10_1016_j_neucom_2024_128627 crossref_primary_10_1016_j_patrec_2025_07_016 crossref_primary_10_1007_s11263_024_02323_0 crossref_primary_10_1145_3735644 crossref_primary_10_1109_TNNLS_2025_3525766 crossref_primary_10_1109_TNNLS_2024_3489585 crossref_primary_10_1109_TETCI_2023_3314551 crossref_primary_10_1109_TNSE_2022_3217185 crossref_primary_10_1007_s12559_023_10146_3 crossref_primary_10_1109_TNNLS_2025_3545435 crossref_primary_10_1016_j_ipm_2023_103603 crossref_primary_10_1016_j_knosys_2023_111020 crossref_primary_10_1109_TAI_2021_3052425 crossref_primary_10_1016_j_inffus_2023_102097 crossref_primary_10_1049_ipr2_12674 crossref_primary_10_1109_TIP_2023_3340609 crossref_primary_10_1016_j_eswa_2025_129019 crossref_primary_10_1109_TNNLS_2023_3261460 crossref_primary_10_1016_j_knosys_2024_111768 crossref_primary_10_1109_TIP_2025_3542602 crossref_primary_10_1016_j_patcog_2024_110716 crossref_primary_10_1007_s00371_022_02717_6 crossref_primary_10_1109_TCYB_2022_3191121 crossref_primary_10_1109_TMM_2024_3521776 crossref_primary_10_1109_TPAMI_2025_3566169 crossref_primary_10_1109_TNNLS_2023_3260258 crossref_primary_10_1109_TNNLS_2022_3189763 crossref_primary_10_1109_TPAMI_2024_3446537 crossref_primary_10_1145_3653305 crossref_primary_10_1016_j_patcog_2022_108957 crossref_primary_10_1109_TIP_2024_3444320 crossref_primary_10_1109_TKDE_2022_3198800 crossref_primary_10_1109_TNNLS_2022_3173742 crossref_primary_10_1016_j_ins_2022_07_089 crossref_primary_10_1016_j_knosys_2025_113119 crossref_primary_10_1145_3534931 crossref_primary_10_1109_TIFS_2025_3539100 crossref_primary_10_1016_j_dsp_2024_104815 crossref_primary_10_1016_j_neucom_2025_130233 crossref_primary_10_1109_TNNLS_2023_3332335 crossref_primary_10_1109_TMM_2024_3521789 crossref_primary_10_1007_s44336_024_00008_3 crossref_primary_10_1109_TETCI_2024_3406704 crossref_primary_10_1109_TPAMI_2023_3318603 crossref_primary_10_1109_TCSVT_2021_3127007 crossref_primary_10_1109_TCSVT_2023_3311174 crossref_primary_10_1007_s13042_024_02270_9 crossref_primary_10_1109_TCSVT_2024_3492814 crossref_primary_10_1016_j_neunet_2025_108003 crossref_primary_10_1109_TCYB_2022_3212480 crossref_primary_10_1109_TMM_2025_3535402 crossref_primary_10_1016_j_inffus_2024_102630 crossref_primary_10_1109_TIP_2023_3310339 crossref_primary_10_1109_TPAMI_2023_3316671 crossref_primary_10_1109_TMM_2023_3260649 crossref_primary_10_1109_TBDATA_2022_3232761 crossref_primary_10_1016_j_patcog_2022_109264 crossref_primary_10_1109_TKDE_2024_3440352 crossref_primary_10_1016_j_neucom_2024_127579 crossref_primary_10_1016_j_neucom_2023_126887 crossref_primary_10_1109_TNNLS_2025_3545465 crossref_primary_10_1109_TNNLS_2023_3279133 crossref_primary_10_1016_j_neucom_2022_06_009 crossref_primary_10_1145_3537900 crossref_primary_10_1109_TCSVT_2023_3302326 crossref_primary_10_1016_j_patcog_2024_110389 crossref_primary_10_1109_TCE_2023_3319018 crossref_primary_10_1109_TETCI_2024_3369316 crossref_primary_10_1016_j_patcog_2025_111977 crossref_primary_10_1109_TCSS_2024_3386621 crossref_primary_10_1109_TPAMI_2022_3231470 crossref_primary_10_1016_j_eswa_2025_128193 crossref_primary_10_1016_j_inffus_2023_03_002 crossref_primary_10_1109_TAI_2024_3373720 crossref_primary_10_1016_j_neunet_2024_107111 crossref_primary_10_1111_tgis_70073 crossref_primary_10_1016_j_patcog_2023_110082 crossref_primary_10_1109_TCSVT_2025_3533301 crossref_primary_10_1109_TIP_2022_3171411 crossref_primary_10_1007_s10462_024_10785_4 crossref_primary_10_1109_TKDE_2025_3579388 crossref_primary_10_1109_TNNLS_2022_3201699 crossref_primary_10_1109_TMM_2023_3323884 crossref_primary_10_1109_TKDE_2023_3312794 crossref_primary_10_1109_TBDATA_2024_3433525 crossref_primary_10_1007_s11432_022_3628_y crossref_primary_10_1016_j_ins_2023_118937 crossref_primary_10_1016_j_neunet_2023_06_038 crossref_primary_10_1109_TMM_2024_3360689 crossref_primary_10_1016_j_ins_2024_121396 crossref_primary_10_1109_TNNLS_2024_3502455 crossref_primary_10_1016_j_neunet_2025_107409 crossref_primary_10_1007_s10489_024_05616_6 crossref_primary_10_1016_j_neucom_2022_05_090 crossref_primary_10_1109_TKDE_2022_3222411 crossref_primary_10_1016_j_patcog_2025_111880 crossref_primary_10_1016_j_patcog_2024_110592 crossref_primary_10_1007_s11042_023_15018_4 crossref_primary_10_1016_j_knosys_2022_110162 crossref_primary_10_1109_TCSVT_2024_3492045 crossref_primary_10_1016_j_patcog_2024_110366 crossref_primary_10_1109_TMM_2023_3340095 crossref_primary_10_1109_TKDE_2024_3487907 crossref_primary_10_1109_TNNLS_2024_3456593 crossref_primary_10_1109_TMM_2024_3521743 crossref_primary_10_1016_j_knosys_2025_114356 crossref_primary_10_1016_j_knosys_2025_113942 crossref_primary_10_1038_s41598_022_17585_2 crossref_primary_10_1109_TKDE_2022_3231929 crossref_primary_10_1109_TKDE_2025_3538852 crossref_primary_10_1016_j_eswa_2025_128413 crossref_primary_10_1109_TCSVT_2022_3200451 crossref_primary_10_1007_s00530_025_01794_6 crossref_primary_10_1109_TETCI_2024_3378603 crossref_primary_10_1109_TCAD_2025_3526060 crossref_primary_10_1109_TKDE_2022_3178145 crossref_primary_10_1109_TNNLS_2024_3373532 crossref_primary_10_1109_TNNLS_2024_3388192 crossref_primary_10_1016_j_patcog_2023_109860 crossref_primary_10_1109_ACCESS_2023_3344462 crossref_primary_10_1109_TCYB_2025_3557917 crossref_primary_10_1016_j_neucom_2023_126320 crossref_primary_10_1016_j_neunet_2025_107669 crossref_primary_10_1109_TAI_2023_3293479 crossref_primary_10_1109_TMM_2020_3019683 crossref_primary_10_1109_TNNLS_2022_3153310 crossref_primary_10_1109_TPAMI_2022_3198411 crossref_primary_10_1109_TPAMI_2024_3398220 crossref_primary_10_1109_TMM_2022_3193855 crossref_primary_10_1145_3568684 crossref_primary_10_1016_j_eswa_2025_126488 crossref_primary_10_1016_j_neunet_2025_107424 crossref_primary_10_1109_TNNLS_2023_3236686 crossref_primary_10_3390_rs14071743 crossref_primary_10_1109_TIP_2021_3131941 crossref_primary_10_1109_TNSE_2024_3389657 crossref_primary_10_1016_j_patcog_2025_112229 crossref_primary_10_1016_j_patcog_2025_111811 crossref_primary_10_1109_TMM_2023_3321499 crossref_primary_10_1007_s10489_025_06476_4 crossref_primary_10_1109_TNNLS_2022_3201562 crossref_primary_10_1016_j_patcog_2025_111818 crossref_primary_10_1016_j_knosys_2025_113960 crossref_primary_10_1109_TETCI_2023_3256466 crossref_primary_10_1109_TKDE_2024_3487534 crossref_primary_10_1109_LSP_2025_3558161 crossref_primary_10_1016_j_neunet_2025_107779 crossref_primary_10_1016_j_neunet_2025_107652 crossref_primary_10_1016_j_ins_2024_120335 crossref_primary_10_1109_TKDE_2024_3413682 crossref_primary_10_1109_TMM_2023_3248173 crossref_primary_10_1109_TNNLS_2024_3486912 crossref_primary_10_1016_j_patcog_2024_110675 crossref_primary_10_1109_TIP_2024_3459651 crossref_primary_10_1016_j_patcog_2025_111844 crossref_primary_10_1007_s10489_022_04074_2 crossref_primary_10_1109_TFUZZ_2023_3306639 crossref_primary_10_1109_TGRS_2021_3074184 crossref_primary_10_1109_TPAMI_2025_3526790 crossref_primary_10_3390_axioms11120722 crossref_primary_10_1145_3694689 crossref_primary_10_1109_TCYB_2022_3157771 crossref_primary_10_1109_TSP_2024_3385654 crossref_primary_10_1109_TC_2024_3416619 crossref_primary_10_1109_TCYB_2024_3443198 crossref_primary_10_1109_TNSE_2023_3244624 crossref_primary_10_1109_TNNLS_2024_3499996 crossref_primary_10_1016_j_knosys_2024_112106 crossref_primary_10_1016_j_inffus_2024_102405 crossref_primary_10_1007_s00530_022_00985_9 crossref_primary_10_1109_TPAMI_2022_3198638 crossref_primary_10_1007_s10489_022_03551_y crossref_primary_10_1109_TMM_2023_3234362 crossref_primary_10_1016_j_inffus_2025_103134 crossref_primary_10_1016_j_neunet_2024_106282 crossref_primary_10_1016_j_neunet_2025_107550 crossref_primary_10_1109_TNNLS_2021_3117403 crossref_primary_10_1016_j_patcog_2025_112124 crossref_primary_10_1109_TSIPN_2024_3414134 crossref_primary_10_1109_TKDE_2024_3443534 crossref_primary_10_1109_TIP_2024_3357257 crossref_primary_10_1109_TKDE_2023_3270311 crossref_primary_10_1109_TNNLS_2024_3392484 crossref_primary_10_1016_j_inffus_2023_101947 crossref_primary_10_1109_TNNLS_2022_3213756 crossref_primary_10_1109_TNNLS_2024_3439394 crossref_primary_10_1109_TNNLS_2024_3378194 crossref_primary_10_1109_TCSVT_2024_3382761 crossref_primary_10_1016_j_inffus_2023_101941 crossref_primary_10_1016_j_neunet_2024_106849 crossref_primary_10_1016_j_knosys_2025_114392 crossref_primary_10_1109_ACCESS_2025_3543099 crossref_primary_10_1109_TPAMI_2025_3532688 crossref_primary_10_1109_TAI_2021_3123126 crossref_primary_10_1109_TNNLS_2024_3442435 crossref_primary_10_1109_TBDATA_2022_3227089 |
| Cites_doi | 10.1023/a:1017501703105 10.1109/TIP.2018.2877335 10.1109/TKDE.2016.2535367 10.1109/TIP.2017.2665976 10.1109/TPAMI.2018.2847335 10.1109/TPAMI.2018.2877660 10.1016/j.knosys.2019.105102 10.1609/aaai.v29i1.9598 10.1109/TCYB.2017.2751646 10.1109/CVPR.2015.7298657 10.1109/34.868688 10.1109/ICCV.2005.148 10.1109/TNNLS.2017.2777489 10.1016/j.cviu.2005.09.012 10.1016/j.inffus.2019.09.005 10.1016/j.neunet.2019.10.010 10.1145/1646396.1646452 10.1609/aaai.v31i1.10867 10.1109/TIP.2016.2553459 10.1137/1.9781611972832.28 10.1109/ICDM.2004.10095 10.1007/s00500-016-2120-3 10.1016/j.knosys.2018.09.009 10.1109/ICCV.2003.1238361 10.1145/183422.183423 10.1609/aaai.v28i1.8950 10.1109/TKDE.2018.2873378 10.1109/TIP.2017.2754939 10.1109/MSP.2012.2211477 10.1109/TMM.2015.2477058 10.1016/j.neucom.2015.01.017 10.24963/ijcai.2017/357 10.1609/aaai.v34i04.5867 10.5244/c.31.64 10.1109/TCYB.2014.2358564 10.1073/pnas.35.11.652 10.1145/1273496.1273642 10.1016/j.neunet.2017.02.003 10.1002/nav.3800020109 10.1109/TCYB.2018.2887094 10.3390/a10040128 10.1609/aaai.v30i1.10302 10.1109/TIP.2015.2463223 10.1016/j.patrec.2009.09.011 10.1137/0715050 10.1109/TKDE.2019.2903810 10.1109/TNNLS.2017.2786743 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TPAMI.2020.3011148 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 344 |
| ExternalDocumentID | 10_1109_TPAMI_2020_3011148 9146384 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China grantid: 2018AAA0101902 funderid: 10.13039/501100012166 – fundername: Inno vation Foundation for Doctor Dissertation of Northwestern Polytechnical University grantid: CX201918 – fundername: National Natural Science Foundation of China grantid: 61871470; 61761130079; 61751202; 61772427; 61936014 funderid: 10.13039/501100001809 |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB ~02 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c328t-d70bc57a464724111fcb53d6ae6ed283c53eb24afff5263d646e9aa619f55a2b3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 356 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000728561300024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 1939-3539 |
| IngestDate | Sun Sep 28 07:34:45 EDT 2025 Mon Jun 30 04:26:53 EDT 2025 Sat Nov 29 05:15:59 EST 2025 Tue Nov 18 22:17:44 EST 2025 Wed Aug 27 05:11:51 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c328t-d70bc57a464724111fcb53d6ae6ed283c53eb24afff5263d646e9aa619f55a2b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-0871-6519 0000-0003-2924-946X 0000-0001-9240-6726 0000-0001-7750-0028 |
| PMID | 32750830 |
| PQID | 2607875752 |
| PQPubID | 85458 |
| PageCount | 15 |
| ParticipantIDs | crossref_primary_10_1109_TPAMI_2020_3011148 proquest_journals_2607875752 proquest_miscellaneous_2430664246 crossref_citationtrail_10_1109_TPAMI_2020_3011148 ieee_primary_9146384 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Jan.-1 2022-1-1 20220101 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-Jan.-1 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationYear | 2022 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref11 ref55 ref10 ref54 Liu (ref40) ref17 ref19 ref18 ref51 ref50 ref48 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref35 ref31 ref30 ref33 Zelnik-Manor (ref36); 17 ref32 Kumar (ref37) ref2 ref1 ref39 ref38 Nie (ref22) Lin (ref47) ref24 Ng (ref34); 14 ref23 ref26 ref25 ref20 ref21 Kumar (ref16) ref28 ref27 Bertsekas (ref45) 1982 ref29 Dheeru (ref52) 2017 Lin (ref46) 2009 Strehl (ref57) 2002; 3 Huang (ref60) |
| References_xml | – start-page: 393 volume-title: Proc. 28th Int. Conf. Mach. Learn. ident: ref37 article-title: A co-training approach for multiview spectral clustering – ident: ref43 doi: 10.1023/a:1017501703105 – ident: ref25 doi: 10.1109/TIP.2018.2877335 – ident: ref42 doi: 10.1109/TKDE.2016.2535367 – ident: ref12 doi: 10.1109/TIP.2017.2665976 – volume: 17 start-page: 1601 volume-title: Proc. 17th Int. Conf. Neural Inf. Process. Syst. ident: ref36 article-title: Self-tuning spectral clustering – ident: ref30 doi: 10.1109/TPAMI.2018.2847335 – ident: ref6 doi: 10.1109/TPAMI.2018.2877660 – ident: ref24 doi: 10.1016/j.knosys.2019.105102 – volume: 3 start-page: 583 year: 2002 ident: ref57 article-title: Cluster ensembles – A knowledge reuse framework for combining multiple partitions publication-title: J. Mach. Learn. Res. – start-page: 1413 volume-title: Proc. 24th Int. Conf. Neural Inf. Process. Syst. ident: ref16 article-title: Co-regularized multiview spectral clustering – ident: ref27 doi: 10.1609/aaai.v29i1.9598 – ident: ref39 doi: 10.1109/TCYB.2017.2751646 – ident: ref4 doi: 10.1109/CVPR.2015.7298657 – ident: ref26 doi: 10.1109/34.868688 – ident: ref51 doi: 10.1109/ICCV.2005.148 – ident: ref17 doi: 10.1109/TNNLS.2017.2777489 – ident: ref53 doi: 10.1016/j.cviu.2005.09.012 – ident: ref21 doi: 10.1016/j.inffus.2019.09.005 – ident: ref9 doi: 10.1016/j.neunet.2019.10.010 – ident: ref55 doi: 10.1145/1646396.1646452 – volume: 14 start-page: 849 volume-title: Proc. 14th Int. Conf. Neural Inf. Process. Syst. ident: ref34 article-title: On spectral clustering: Analysis and an algorithm – ident: ref13 doi: 10.1609/aaai.v31i1.10867 – ident: ref19 doi: 10.1109/TIP.2016.2553459 – start-page: 679 volume-title: Proc. 27th Int. Conf. Mach. Learn. ident: ref40 article-title: Large graph construction for scalable semi-supervised learning – volume-title: Constrained Optimization and Lagrange Multiplier Methods year: 1982 ident: ref45 – ident: ref10 doi: 10.1137/1.9781611972832.28 – ident: ref14 doi: 10.1109/ICDM.2004.10095 – ident: ref28 doi: 10.1007/s00500-016-2120-3 – start-page: 1881 volume-title: Proc. 25th Int. Joint Conf. Artif. Intell. ident: ref22 article-title: Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification – ident: ref32 doi: 10.1016/j.knosys.2018.09.009 – start-page: 3569 volume-title: Proc. 24th Int. Joint Conf. Artif. Intell. ident: ref60 article-title: A new simplex sparse learning model to measure data similarity for clustering – ident: ref35 doi: 10.1109/ICCV.2003.1238361 – ident: ref56 doi: 10.1145/183422.183423 – ident: ref18 doi: 10.1609/aaai.v28i1.8950 – ident: ref20 doi: 10.1109/TKDE.2018.2873378 – ident: ref23 doi: 10.1109/TIP.2017.2754939 – start-page: 612 volume-title: Proc. 24th Int. Conf. Neural Inf. Process. Syst. ident: ref47 article-title: Linearized alternating direction method with adaptive penalty for low-rank representation – ident: ref54 doi: 10.1109/MSP.2012.2211477 – ident: ref2 doi: 10.1109/TMM.2015.2477058 – ident: ref5 doi: 10.1016/j.neucom.2015.01.017 – ident: ref38 doi: 10.24963/ijcai.2017/357 – ident: ref8 doi: 10.1609/aaai.v34i04.5867 – ident: ref1 doi: 10.5244/c.31.64 – ident: ref41 doi: 10.1109/TCYB.2014.2358564 – ident: ref44 doi: 10.1073/pnas.35.11.652 – ident: ref15 doi: 10.1145/1273496.1273642 – ident: ref11 doi: 10.1016/j.neunet.2017.02.003 – ident: ref58 doi: 10.1002/nav.3800020109 – year: 2017 ident: ref52 article-title: UCI machine learning repository – ident: ref31 doi: 10.1109/TCYB.2018.2887094 – ident: ref48 doi: 10.3390/a10040128 – year: 2009 ident: ref46 article-title: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices – ident: ref50 doi: 10.1109/TCYB.2014.2358564 – ident: ref33 doi: 10.1609/aaai.v30i1.10302 – ident: ref3 doi: 10.1109/TIP.2015.2463223 – ident: ref49 doi: 10.1016/j.patrec.2009.09.011 – ident: ref59 doi: 10.1137/0715050 – ident: ref29 doi: 10.1109/TKDE.2019.2903810 – ident: ref7 doi: 10.1109/TNNLS.2017.2786743 |
| SSID | ssj0014503 |
| Score | 2.720541 |
| Snippet | Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 330 |
| SubjectTerms | Algorithms Bipartite graph Clustering Clustering algorithms Computer science connectivity constraint Data models Electronic mail graph fusion Graph theory Graphs initialization-independent Laplace equations Mathematical models Multiview clustering Optical imaging Parameters Regularization scalable and parameter-free |
| Title | Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method |
| URI | https://ieeexplore.ieee.org/document/9146384 https://www.proquest.com/docview/2607875752 https://www.proquest.com/docview/2430664246 |
| Volume | 44 |
| WOSCitedRecordID | wos000728561300024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fa9RAEB7a4oM-WG2Vntaygm8am-6v7Pp2Fk99aDmhwr2FzWYWCkeuXC_-_c7s5UJBEXwL2UkIOzPZ-XZ25gN4JxtnEzamiGVsC-0dFt6li8KmJsikK1rWUyabqK6v3WLh53vwYayFQcR8-Aw_8mXO5ber2PNW2bknt1ZO78N-VVXbWq0xY6BNZkGmCIY8nGDErkCm9Oc38-nVd4KCkhAqU6trJulT3Njc8eHnB-tRJlj546-cl5rZ4f995DN4OoSUYrq1geewh90RHO7oGsTgvUfw5EHvwWP4kUtvOTEgLpc9t0ug25_ElOTDkuupROhaMQ98eIsGi9kaUXy-vWNT26D4yo2uxaznzTZxlWmoX8DP2Zeby2_FwK9QRCXdpmirsommCppbyGuamBQbo1ob0GJLYUc0inC3DiklIy0NaIs-BIJcyZggG_USDrpVhycgkm61UdJH6YIu0Tr0ySrdRkcRXKPUBC52s1zHofk4c2As6wxCSl9nJdWspHpQ0gTej8_cbVtv_FP6mHUxSg5qmMDpTpn14J33NWG4ihv5GzmBt-Mw-RUnS0KHq55kNIEpAmfavvr7m1_DY8mlEHk75hQONuse38Cj-Gtze78-IxNduLNsor8BEw_faQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED6NgQQ8MNhAFAYYiTfIlvpX7L11E2UTa1WkIu0tchxbmlSlU9fw93PnptEk0KS9RbETRT5ffJ_P930AX3hldAyVynzu60xaEzJr4jDTsXI8ygKX9ZjEJorp1Fxd2dkOfOtrYUII6fBZOKLLlMuvl76lrbJji24tjHwEj5WUfLip1upzBlIlHWSMYdDHEUhsS2RyezyfjSYXCAY5YlQSV5ck0yeI2tzQ8ec7K1KSWPnnv5wWm_Hewz7zJbzogko22syCV7ATmn3Y2wo2sM5_9-H5HfbBA_iVim8pNcDOFi0RJuDtEzbC_m5BFVXMNTWbOTq-hY3ZeBUCO72-ocm2DuwHUV2zcUvbbWyShKhfw-_x9_nZedYpLGRecLPO6iKvvCqcJBJ5iQMTfaVErV3QocbAwyuByFu6GKPiGhukDtY5BF1RKccr8QZ2m2UT3gKLspZKcOu5cTIP2gQbtZC1NxjDVUIMYLgd5dJ39OOkgrEoEwzJbZmMVJKRys5IA_jaP3OzId-4t_cB2aLv2ZlhAIdbY5adf96WiOIKovJXfACf-2b0LEqXuCYsW-wjEU4hPJP63f_f_Amens8nl-XlxfTne3jGqTAibc4cwu561YYP8MT_WV_frj6mifoXlyPhyA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multiview+Clustering%3A+A+Scalable+and+Parameter-Free+Bipartite+Graph+Fusion+Method&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Li%2C+Xuelong&rft.au=Zhang%2C+Han&rft.au=Wang%2C+Rong&rft.au=Nie%2C+Feiping&rft.date=2022-01-01&rft.pub=IEEE&rft.issn=0162-8828&rft.volume=44&rft.issue=1&rft.spage=330&rft.epage=344&rft_id=info:doi/10.1109%2FTPAMI.2020.3011148&rft_id=info%3Apmid%2F32750830&rft.externalDocID=9146384 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |