Progressive graph-based subspace transductive learning for semi-supervised classification
Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature inform...
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| Veröffentlicht in: | IET image processing Jg. 13; H. 14; S. 2753 - 2762 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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The Institution of Engineering and Technology
12.12.2019
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| ISSN: | 1751-9659, 1751-9667 |
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| Abstract | Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature information with label information. To address these issues, we propose an approach called Progressive Graph-based subspace transductive learning (PGSTL) in this paper. PGSTL gradually find the intrinsic relationship between samples that more accurately aligns feature with label. Meanwhile, PGSTL develops a feature affinity matrix in the subspace of original high-dimensional feature space, which effectively reduce the interference of noise points. And then, the representative relation matrix and the feature affinity matrix are optimized by iterative optimization strategy and finally aligned. In this way, PGSTL can not only effectively reduce the interference of noisy points, but also comprehensively consider the information in the feature and label domain of data. Extensive experimental results on various benchmark datasets demonstrate that the PGSTL achieves the best performance compared to some state-of-the-art semi-supervised learning methods. |
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| AbstractList | Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature information with label information. To address these issues, we propose an approach called Progressive Graph-based subspace transductive learning (PGSTL) in this paper. PGSTL gradually find the intrinsic relationship between samples that more accurately aligns feature with label. Meanwhile, PGSTL develops a feature affinity matrix in the subspace of original high-dimensional feature space, which effectively reduce the interference of noise points. And then, the representative relation matrix and the feature affinity matrix are optimized by iterative optimization strategy and finally aligned. In this way, PGSTL can not only effectively reduce the interference of noisy points, but also comprehensively consider the information in the feature and label domain of data. Extensive experimental results on various benchmark datasets demonstrate that the PGSTL achieves the best performance compared to some state-of-the-art semi-supervised learning methods. |
| Author | Zhong, Zhi Chen, Long |
| Author_xml | – sequence: 1 givenname: Long surname: Chen fullname: Chen, Long organization: 2School of Computer and Information Engineering, Nanning Normal University, Nanning, 530000, People's Republic of China – sequence: 2 givenname: Zhi surname: Zhong fullname: Zhong, Zhi email: 1063477512@qq.com organization: 2School of Computer and Information Engineering, Nanning Normal University, Nanning, 530000, People's Republic of China |
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| Cites_doi | 10.1016/j.patrec.2014.02.020 10.1109/TGRS.2018.2864987 10.1016/j.patcog.2011.02.013 10.1007/978-3-319-46484-8_35 10.1016/j.media.2017.05.003 10.1109/TNNLS.2014.2363679 10.1137/1.9781611972795.68 10.1145/1273496.1273571 10.1145/2623330.2623726 10.1007/s10115-013-0702-2 10.1109/TMM.2017.2703636 10.1109/CVPR.2009.5206871 10.1007/s11042-017-5272-y 10.1023/A:1018628609742 10.1109/MSP.2010.936015 10.1023/B:MACH.0000033120.25363.1e 10.1007/s11042-017-5381-7 10.1109/TMM.2017.2729019 10.1007/978-3-642-40994-3_11 10.1016/j.ins.2014.02.067 10.3724/SP.J.1004.2012.01335 10.1109/TIP.2009.2038764 10.1109/TPAMI.2019.2894139 10.1109/ICCV.2013.218 10.1016/j.neucom.2018.10.027 10.2307/2346830 10.1016/j.knosys.2014.12.014 10.1016/j.patcog.2016.03.020 10.1145/1039621.1039623 10.3115/1219840.1219889 10.1109/TKDE.2017.2763618 10.1016/j.neucom.2015.11.112 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Image Processing published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | label domain pattern classification iterative methods sufficient labelled samples representative relation matrix graph theory high-dimensional feature space feature information semisupervised classification feature affinity matrix matrix algebra noise points progressive graph-based subspace transductive learning feature relationships optimisation feature domain efficient semisupervised learning technique fixed subject-wise graph feature-to-label alignment learning (artificial intelligence) PGSTL iterative optimisation strategy label information |
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| References | Suykens, J.A.K.; Vandewalle, J. (C1) 1999; 9 Hady, M.F.A.; Schwenker, F. (C8) 2006; 172 Lei, C.; Zhu, X. (C30) 2018; 77 Zheng, W.; Zhu, X.; Wen, G. (C33) 2018 Zhu, X.; Zhang, S.; Hu, R. (C5) 2018 Shi, C.; Duan, C.; Gu, Z. (C11) 2019; 330 Belkin, M.; Niyogi, P.; Sindhwani, V. (C22) 2006; 7 Yuan, Y.; Wan, J.; Wang, Q. (C51) 2016; 56 Zhu, X.; Zhang, S.; Hu, R. (C6) 2018; 30 Belkin, M.; Niyogi, P. (C16) 2004; 56 Song, J.; Guo, Y.; Gao, L. (C46) 2018 Fan, M.; Gu, N.; Qiao, H. (C28) 2011; 44 Kim, K.H.; Choi, S. (C44) 2014; 45 Li, B.; Qin, L.; Yu, S. (C2) 2004; 3 Getz, G.; Shental, N.; Domany, E. (C37) 2006 Zhu, X.; Goldberg, A.B.; Brachman, R. (C9) 2009; 3 Zhou, L.; Ping, X.; Xu, S. (C4) 2012; 38 Wang, Q.; Liu, S.; Chanussot, J. (C50) 2019; 57 Cheng, B.; Yang, J.; Yan, S. (C39) 2010; 19 Zheng, W.; Zhu, X.; Zhu, Y. (C29) 2018; 77 Peng, X.; Yuan, M.; Yu, Z. (C42) 2016; 208 Zhang, Y.M.; Huang, K.; Geng, G.G. (C15) 2015; 26 Peng, J.; Estradab, G.; Pedersoli, M. (C10) 2019 Gao, L.; Li, X.; Song, J. (C47) 2019 Xiaofeng Zhu, Y.L.J.Z.L.Y.; Zhang, S.; Fang, Y. (C54) 2018; 31 Gershman, A.B.; Sidiropoulos, N.D.; Shahbazpanahi, S. (C55) 2010; 27 Liu, X.; Pan, S.; Hao, Z. (C43) 2014; 277 Yu, G.; Zhang, G.; Zhang, Z. (C41) 2015; 43 Gao, L.; Guo, Z.; Zhang, H. (C48) 2017; 19 Hartigan, J.A. (C3) 1979; 28 Wang, B.; Tsotsos, J. (C45) 2016; 52 Li, Y.F.; Tsang, I.W.; Kwok, J.T. (C23) 2013; 14 Zhu, X.; Li, X.; Zhang, S. (C24) 2017; 19 Wang, Z.; Zhu, X.; Adeli, E. (C36) 2017; 39 Rosenberg, C.; Hebert, M.; Schneiderman, H. (C14) 2005; 1 Zhao, M.; Chow, T.W.S.; Zhang, Z. (C34) 2015; 76 2010; 19 2016; 208 2019; 57 2015; 76 2006; 7 2009 2004; 3 2007 2016; 52 2006 2005 2004 2012; 38 2006; 172 2003 2002 2014; 45 2014; 277 2016; 56 1999; 9 1979; 28 2015; 26 2010; 27 2013; 14 2001 2000 2017; 39 2004; 56 2015; 43 2019 2018 2011; 44 2017 2018; 30 2005; 1 2017; 19 2016 2014 2013 2009; 3 2018; 77 2018; 31 2019; 330 e_1_2_8_28_1 e_1_2_8_49_1 Belkin M. (e_1_2_8_23_1) 2006; 7 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 Belkin M. (e_1_2_8_26_1) 2004 Song J. (e_1_2_8_47_1) 2018 e_1_2_8_57_1 Zhu X. (e_1_2_8_10_1) 2009; 3 e_1_2_8_32_1 Getz G. (e_1_2_8_38_1) 2006 e_1_2_8_53_1 e_1_2_8_51_1 Li Y.F. (e_1_2_8_24_1) 2013; 14 e_1_2_8_30_1 Wang B. (e_1_2_8_46_1) 2016; 52 e_1_2_8_29_1 Zheng W. (e_1_2_8_34_1) 2018 e_1_2_8_25_1 Xiaofeng Zhu Y.L.J.Z.L.Y. (e_1_2_8_55_1) 2018; 31 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 Peng J. (e_1_2_8_11_1) 2019 Rosenberg C. (e_1_2_8_15_1) 2005; 1 e_1_2_8_8_1 Hady M.F.A. (e_1_2_8_9_1) 2006; 172 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_58_1 Zhu X. (e_1_2_8_6_1) 2018 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 e_1_2_8_50_1 |
| References_xml | – volume: 30 start-page: 517 issue: 3 year: 2018 end-page: 529 ident: C6 article-title: Local and global structure preservation for robust unsupervised spectral feature selection publication-title: IEEE Trans. Knowl. Data Eng. – volume: 3 start-page: 215 issue: 4 year: 2004 end-page: 226 ident: C2 article-title: An adaptive k-nearest neighbor text categorization strategy publication-title: ACM Trans. Asian Lang. Inf. Process. – volume: 172 start-page: 530 issue: 2 year: 2006 end-page: 530 ident: C8 article-title: Semi-supervised learning publication-title: J. Royal Stat. Soc. – volume: 19 start-page: 2045 issue: 9 year: 2017 end-page: 2055 ident: C48 article-title: Video captioning with attention-based LSTM and semantic consistency publication-title: IEEE Trans. Multimed. – year: 2018 ident: C33 article-title: Unsupervised feature selection by self-paced learning regularization publication-title: Pattern Recognit. Lett. – volume: 1 start-page: 29 year: 2005 end-page: 36 ident: C14 article-title: Semi-supervised self-training of object detection models publication-title: IEEE Int. Conf. Comput. Vis. – year: 2019 ident: C10 article-title: Deep co-training for semi-supervised image segmentation publication-title: CoRR – volume: 277 start-page: 327 issue: 2 year: 2014 end-page: 337 ident: C43 article-title: Graph-based semi-supervised learning by mixed label propagation with a soft constraint publication-title: Inf. Sci. – year: 2006 ident: C37 article-title: Semi-supervised learning – a statistical physics approach publication-title: Comput. Sci. – volume: 330 start-page: 412 year: 2019 end-page: 424 ident: C11 article-title: Semi-supervised feature selection analysis with structured multi-view sparse regularization publication-title: Neurocomputing – volume: 31 start-page: 1532 issue: 8 year: 2018 end-page: 1543 ident: C54 article-title: Low-rank sparse subspace for spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 3 start-page: 130 issue: 1 year: 2009 ident: C9 article-title: Introduction to semisupervised learning publication-title: Semi-Supervised Learn. – volume: 52 start-page: 75 year: 2016 end-page: 84 ident: C45 article-title: Dynamic label propagation for semi-supervised multiclass multi-label classification publication-title: Elsevier Sci. Inc. – volume: 57 start-page: 1155 issue: 2 year: 2019 end-page: 1167 ident: C50 article-title: Scene classification with recurrent attention of VHR remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 76 start-page: 148 year: 2015 end-page: 165 ident: C34 article-title: Automatic image annotation via compact graph based semi-supervised learning publication-title: Knowl.-Based Syst. – volume: 9 start-page: 293 issue: 3 year: 1999 end-page: 300 ident: C1 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. – volume: 28 start-page: 100 issue: 1 year: 1979 end-page: 108 ident: C3 article-title: A k-means clustering algorithm publication-title: Appl. Stat. – volume: 38 start-page: 1335 issue: 38 year: 2012 end-page: 1342 ident: C4 article-title: Cluster ensemble based on spectral clustering publication-title: Acta Autom. Sin. – year: 2018 ident: C46 article-title: From deterministic to generative: multimodal stochastic rnns for video captioning publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 14 start-page: 2151 issue: 1 year: 2013 end-page: 2188 ident: C23 article-title: Convex and scalable weakly labeled svms publication-title: J. Mach. Learn. Res. – year: 2018 ident: C5 article-title: One-step multi-view spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 45 start-page: 17 issue: 1 year: 2014 end-page: 25 ident: C44 article-title: Label propagation through minimax paths for scalable semi-supervised learning publication-title: Pattern Recognit. Lett. – volume: 56 start-page: 209 issue: 1–3 year: 2004 end-page: 239 ident: C16 article-title: Semi-supervised learning on Riemannian manifolds publication-title: Mach. Learn. – volume: 7 start-page: 2399 year: 2006 end-page: 2434 ident: C22 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – volume: 43 start-page: 81 issue: 1 year: 2015 end-page: 101 ident: C41 article-title: Semi-supervised classification based on subspace sparse representation publication-title: Knowl. Inf. Syst. – volume: 44 start-page: 1777 issue: 8 year: 2011 end-page: 1784 ident: C28 article-title: Sparse regularization for semi-supervised classification publication-title: Pattern Recognit. – year: 2019 ident: C47 article-title: Hierarchical LSTMS with adaptive attention for visual captioning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 19 start-page: 858 issue: 4 year: 2010 end-page: 866 ident: C39 article-title: Learning with l1-graph for image analysis publication-title: IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. – volume: 77 start-page: 29739 issue: 22 year: 2018 end-page: 29755 ident: C29 article-title: Dynamic graph learning for spectral feature selection publication-title: Multimedia Tools Appl. – volume: 39 start-page: 218 year: 2017 ident: C36 article-title: ADNI and PPMI: multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning publication-title: Med. Image Anal. – volume: 27 start-page: 62 issue: 3 year: 2010 end-page: 75 ident: C55 article-title: Convex optimization-based beamforming publication-title: IEEE Signal Process. Mag. – volume: 77 start-page: 29605 issue: 22 year: 2018 end-page: 29622 ident: C30 article-title: Unsupervised feature selection via local structure learning and sparse learning publication-title: Multimedia Tools Appl. – volume: 26 start-page: 1979 issue: 9 year: 2015 end-page: 1991 ident: C15 article-title: MTC: a fast and robust graph-based transductive learning method publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 19 start-page: 2033 issue: 9 year: 2017 end-page: 2044 ident: C24 article-title: Graph PCA hashing for similarity search publication-title: IEEE Trans. Multimed. – volume: 56 start-page: 159 year: 2016 end-page: 169 ident: C51 article-title: Congested scene classification via efficient unsupervised feature learning and density estimation publication-title: Pattern Recognit. – volume: 208 start-page: 143 issue: C year: 2016 end-page: 152 ident: C42 article-title: Semi-supervised subspace learning with l2graph publication-title: Neurocomputing – volume: 208 start-page: 143 issue: C year: 2016 end-page: 152 article-title: Semi‐supervised subspace learning with l2graph publication-title: Neurocomputing – volume: 27 start-page: 62 issue: 3 year: 2010 end-page: 75 article-title: Convex optimization‐based beamforming publication-title: IEEE Signal Process. Mag. – volume: 56 start-page: 159 year: 2016 end-page: 169 article-title: Congested scene classification via efficient unsupervised feature learning and density estimation publication-title: Pattern Recognit. – volume: 172 start-page: 530 issue: 2 year: 2006 end-page: 530 article-title: Semi‐supervised learning publication-title: J. Royal Stat. Soc. – volume: 19 start-page: 2033 issue: 9 year: 2017 end-page: 2044 article-title: Graph PCA hashing for similarity search publication-title: IEEE Trans. Multimed. – start-page: 624 year: 2004 end-page: 638 – volume: 3 start-page: 130 issue: 1 year: 2009 article-title: Introduction to semisupervised learning publication-title: Semi‐Supervised Learn. – volume: 39 start-page: 218 year: 2017 article-title: ADNI and PPMI: multi‐modal classification of neurodegenerative disease by progressive graph‐based transductive learning publication-title: Med. Image Anal. – volume: 76 start-page: 148 year: 2015 end-page: 165 article-title: Automatic image annotation via compact graph based semi‐supervised learning publication-title: Knowl.‐Based Syst. – volume: 57 start-page: 1155 issue: 2 year: 2019 end-page: 1167 article-title: Scene classification with recurrent attention of VHR remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 45 start-page: 17 issue: 1 year: 2014 end-page: 25 article-title: Label propagation through minimax paths for scalable semi‐supervised learning publication-title: Pattern Recognit. Lett. – start-page: 381 year: 2009 end-page: 388 – start-page: 1881 year: 2016 end-page: 1887 – volume: 7 start-page: 2399 year: 2006 end-page: 2434 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – start-page: 601 year: 2002 end-page: 608 – start-page: 1737 year: 2013 end-page: 1744 – start-page: 86 year: 2000 end-page: 93 – start-page: 2408 year: 2017 end-page: 2414 – volume: 26 start-page: 1979 issue: 9 year: 2015 end-page: 1991 article-title: MTC: a fast and robust graph‐based transductive learning method publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 792 year: 2009 end-page: 801 – year: 2018 article-title: Unsupervised feature selection by self‐paced learning regularization publication-title: Pattern Recognit. Lett. – volume: 19 start-page: 858 issue: 4 year: 2010 end-page: 866 article-title: Learning with l1‐graph for image analysis publication-title: IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. – start-page: 321 year: 2003 end-page: 328 – volume: 77 start-page: 29739 issue: 22 year: 2018 end-page: 29755 article-title: Dynamic graph learning for spectral feature selection publication-title: Multimedia Tools Appl. – year: 2018 article-title: One‐step multi‐view spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 2465 year: 2005 end-page: 2472 – volume: 3 start-page: 215 issue: 4 year: 2004 end-page: 226 article-title: An adaptive k‐nearest neighbor text categorization strategy publication-title: ACM Trans. Asian Lang. Inf. Process. – volume: 14 start-page: 2151 issue: 1 year: 2013 end-page: 2188 article-title: Convex and scalable weakly labeled svms publication-title: J. Mach. Learn. Res. – year: 2006 article-title: Semi‐supervised learning – a statistical physics approach publication-title: Comput. Sci. – volume: 19 start-page: 2045 issue: 9 year: 2017 end-page: 2055 article-title: Video captioning with attention‐based LSTM and semantic consistency publication-title: IEEE Trans. Multimed. – start-page: 593 year: 2007 end-page: 600 – start-page: 19 year: 2001 end-page: 26 – volume: 43 start-page: 81 issue: 1 year: 2015 end-page: 101 article-title: Semi‐supervised classification based on subspace sparse representation publication-title: Knowl. Inf. Syst. – volume: 44 start-page: 1777 issue: 8 year: 2011 end-page: 1784 article-title: Sparse regularization for semi‐supervised classification publication-title: Pattern Recognit. – volume: 77 start-page: 29605 issue: 22 year: 2018 end-page: 29622 article-title: Unsupervised feature selection via local structure learning and sparse learning publication-title: Multimedia Tools Appl. – start-page: 395 year: 2005 end-page: 402 – volume: 277 start-page: 327 issue: 2 year: 2014 end-page: 337 article-title: Graph‐based semi‐supervised learning by mixed label propagation with a soft constraint publication-title: Inf. Sci. – start-page: 583 year: 2016 end-page: 598 – volume: 38 start-page: 1335 issue: 38 year: 2012 end-page: 1342 article-title: Cluster ensemble based on spectral clustering publication-title: Acta Autom. Sin. – volume: 9 start-page: 293 issue: 3 year: 1999 end-page: 300 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. – year: 2003 – start-page: 977 year: 2014 end-page: 986 – volume: 31 start-page: 1532 issue: 8 year: 2018 end-page: 1543 article-title: Low‐rank sparse subspace for spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 1 start-page: 29 year: 2005 end-page: 36 article-title: Semi‐supervised self‐training of object detection models publication-title: IEEE Int. Conf. Comput. Vis. – year: 2019 article-title: Hierarchical LSTMS with adaptive attention for visual captioning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 912 year: 2003 end-page: 919 – start-page: 160 year: 2013 end-page: 175 – volume: 30 start-page: 517 issue: 3 year: 2018 end-page: 529 article-title: Local and global structure preservation for robust unsupervised spectral feature selection publication-title: IEEE Trans. Knowl. Data Eng. – year: 2019 article-title: Deep co‐training for semi‐supervised image segmentation publication-title: CoRR – volume: 56 start-page: 209 issue: 1–3 year: 2004 end-page: 239 article-title: Semi‐supervised learning on Riemannian manifolds publication-title: Mach. Learn. – volume: 28 start-page: 100 issue: 1 year: 1979 end-page: 108 article-title: A k‐means clustering algorithm publication-title: Appl. Stat. – start-page: 441 year: 2009 end-page: 448 – volume: 52 start-page: 75 year: 2016 end-page: 84 article-title: Dynamic label propagation for semi‐supervised multiclass multi‐label classification publication-title: Elsevier Sci. Inc. – volume: 330 start-page: 412 year: 2019 end-page: 424 article-title: Semi‐supervised feature selection analysis with structured multi‐view sparse regularization publication-title: Neurocomputing – start-page: 290 year: 2003 end-page: 297 – year: 2017 – year: 2018 article-title: From deterministic to generative: multimodal stochastic rnns for video captioning publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 2018 ident: e_1_2_8_47_1 article-title: From deterministic to generative: multimodal stochastic rnns for video captioning publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 1 start-page: 29 year: 2005 ident: e_1_2_8_15_1 article-title: Semi‐supervised self‐training of object detection models publication-title: IEEE Int. Conf. Comput. Vis. – ident: e_1_2_8_45_1 doi: 10.1016/j.patrec.2014.02.020 – ident: e_1_2_8_14_1 – ident: e_1_2_8_51_1 doi: 10.1109/TGRS.2018.2864987 – ident: e_1_2_8_29_1 doi: 10.1016/j.patcog.2011.02.013 – ident: e_1_2_8_41_1 doi: 10.1007/978-3-319-46484-8_35 – ident: e_1_2_8_59_1 – ident: e_1_2_8_22_1 – ident: e_1_2_8_37_1 doi: 10.1016/j.media.2017.05.003 – volume: 52 start-page: 75 year: 2016 ident: e_1_2_8_46_1 article-title: Dynamic label propagation for semi‐supervised multiclass multi‐label classification publication-title: Elsevier Sci. Inc. – ident: e_1_2_8_32_1 – ident: e_1_2_8_16_1 doi: 10.1109/TNNLS.2014.2363679 – ident: e_1_2_8_13_1 – ident: e_1_2_8_28_1 doi: 10.1137/1.9781611972795.68 – ident: e_1_2_8_33_1 doi: 10.1145/1273496.1273571 – year: 2019 ident: e_1_2_8_11_1 article-title: Deep co‐training for semi‐supervised image segmentation publication-title: CoRR – volume: 31 start-page: 1532 issue: 8 year: 2018 ident: e_1_2_8_55_1 article-title: Low‐rank sparse subspace for spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – ident: e_1_2_8_36_1 doi: 10.1145/2623330.2623726 – ident: e_1_2_8_42_1 doi: 10.1007/s10115-013-0702-2 – ident: e_1_2_8_25_1 doi: 10.1109/TMM.2017.2703636 – ident: e_1_2_8_27_1 doi: 10.1109/CVPR.2009.5206871 – ident: e_1_2_8_30_1 doi: 10.1007/s11042-017-5272-y – ident: e_1_2_8_2_1 doi: 10.1023/A:1018628609742 – ident: e_1_2_8_56_1 doi: 10.1109/MSP.2010.936015 – ident: e_1_2_8_17_1 doi: 10.1023/B:MACH.0000033120.25363.1e – ident: e_1_2_8_18_1 – ident: e_1_2_8_31_1 doi: 10.1007/s11042-017-5381-7 – year: 2018 ident: e_1_2_8_34_1 article-title: Unsupervised feature selection by self‐paced learning regularization publication-title: Pattern Recognit. Lett. – year: 2006 ident: e_1_2_8_38_1 article-title: Semi‐supervised learning – a statistical physics approach publication-title: Comput. Sci. – ident: e_1_2_8_49_1 doi: 10.1109/TMM.2017.2729019 – ident: e_1_2_8_54_1 doi: 10.1007/978-3-642-40994-3_11 – ident: e_1_2_8_44_1 doi: 10.1016/j.ins.2014.02.067 – ident: e_1_2_8_53_1 – ident: e_1_2_8_5_1 doi: 10.3724/SP.J.1004.2012.01335 – ident: e_1_2_8_50_1 – year: 2018 ident: e_1_2_8_6_1 article-title: One‐step multi‐view spectral clustering publication-title: IEEE Trans. Knowl. Data Eng. – ident: e_1_2_8_40_1 doi: 10.1109/TIP.2009.2038764 – volume: 3 start-page: 130 issue: 1 year: 2009 ident: e_1_2_8_10_1 article-title: Introduction to semisupervised learning publication-title: Semi‐Supervised Learn. – ident: e_1_2_8_48_1 doi: 10.1109/TPAMI.2019.2894139 – ident: e_1_2_8_20_1 – ident: e_1_2_8_58_1 doi: 10.1109/ICCV.2013.218 – start-page: 624 volume-title: Regularization and semi‐supervised learning on large graphs year: 2004 ident: e_1_2_8_26_1 – ident: e_1_2_8_12_1 doi: 10.1016/j.neucom.2018.10.027 – ident: e_1_2_8_21_1 – ident: e_1_2_8_4_1 doi: 10.2307/2346830 – ident: e_1_2_8_35_1 doi: 10.1016/j.knosys.2014.12.014 – ident: e_1_2_8_52_1 doi: 10.1016/j.patcog.2016.03.020 – volume: 14 start-page: 2151 issue: 1 year: 2013 ident: e_1_2_8_24_1 article-title: Convex and scalable weakly labeled svms publication-title: J. Mach. Learn. Res. – ident: e_1_2_8_3_1 doi: 10.1145/1039621.1039623 – ident: e_1_2_8_19_1 – ident: e_1_2_8_39_1 doi: 10.3115/1219840.1219889 – volume: 172 start-page: 530 issue: 2 year: 2006 ident: e_1_2_8_9_1 article-title: Semi‐supervised learning publication-title: J. Royal Stat. Soc. – ident: e_1_2_8_8_1 – volume: 7 start-page: 2399 year: 2006 ident: e_1_2_8_23_1 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – ident: e_1_2_8_57_1 – ident: e_1_2_8_7_1 doi: 10.1109/TKDE.2017.2763618 – ident: e_1_2_8_43_1 doi: 10.1016/j.neucom.2015.11.112 |
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| SubjectTerms | efficient semisupervised learning technique feature affinity matrix feature domain feature information feature relationships feature‐to‐label alignment fixed subject‐wise graph graph theory high‐dimensional feature space iterative methods iterative optimisation strategy label domain label information learning (artificial intelligence) matrix algebra noise points optimisation pattern classification PGSTL progressive graph‐based subspace transductive learning representative relation matrix semisupervised classification Special Section: Adversarial Learning in Image Processing sufficient labelled samples |
| Title | Progressive graph-based subspace transductive learning for semi-supervised classification |
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