Výsledky vyhľadávania - Multi-view data/clustering

Sampling Enhanced Contrastive Multi-View Remote Sensing Data Clustering With Long-Short Range Information Mining

Autori: Renxiang Guan Tianrui Liu Wenxuan Tu a ďalší

Zdroj: IEEE Transactions on Knowledge and Data Engineering. 37:5598-5612

A Multi-Kernel-Based Multi-View Deep Non-Negative Matrix Factorization for Enhanced Healthcare Data Clustering

Autori: Hangjun Che Xuanhao Yang

Zdroj: IEEE Transactions on Consumer Electronics. 71:1442-1452

Multi-View Adaptive Fusion Network for Spatially Resolved Transcriptomics Data Clustering

Autori: Yanran Zhu Xiao He Chang Tang a ďalší

Zdroj: IEEE Transactions on Knowledge and Data Engineering. 36:8889-8900

Feature decomposition and structural learning for multi-diverse and multi-view data clustering

Autori: Yong Zhang Da Liu Li Jiang a ďalší

Zdroj: The Visual Computer. 41:4301-4320

An End-to-End Approach for Graph-Based Multi-View Data Clustering

Autori: Fadi Dornaika Sally El Hajjar

Zdroj: IEEE Transactions on Big Data. 10:644-654

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering

Autori: Xiaoyan Yu Yixuan Ren Min Xia a ďalší

Zdroj: Brief Bioinform

Predmety: Machine Learning, Sequence Analysis, RNA, Problem Solving Protocol, Cluster Analysis, Humans, Computational Biology, Animals, Neural Networks, Computer, RNA-Seq, Single-Cell Analysis, Algorithms, Single-Cell Gene Expression Analysis

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40366859

Orkm: An R Package for Online Multi-View Data Clustering

Autori: Guangbao Guo Miao Yu Shu Li

A multi-view comparative learning method for spatial transcriptomics data clustering

Autori: Wei Peng Zhihao Zhang Wei Dai a ďalší

Zdroj: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :287-292

Soft Computing Based Multi-View High Dimensional Data Clustering Algorithm for Big Data.

Autori: CHUNDRU, RAJA RAMESH NAYAK, RAKESH JAGANNATHAN, VEERAJRAGHAVAN a ďalší

Zdroj: Journal of Information Science & Engineering. Jul2025, Vol. 41 Issue 4, p1031-1046. 16p.

Predmety: Clustering algorithms, Gaussian mixture models, Particle swarm optimization, Soft computing, Graph algorithms

Unified Multi-view Data Clustering: Simultaneous Learning of Consensus Coefficient Matrix and Similarity Graph

Autori: F. Dornaika S. El Hajjar J. Charafeddine a ďalší

Zdroj: Cognitive Computation. 17

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering

Autori: Shu, Zhenqiu Xia, Min Tan, Kaiwen a ďalší

Zdroj: Brief Bioinform

Predmety: Machine Learning, Sequence Analysis, RNA, Problem Solving Protocol, Cluster Analysis, Humans, Computational Biology, RNA-Seq, Single-Cell Analysis, Algorithms, Single-Cell Gene Expression Analysis

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39494609

Multi-View Data Clustering via Dynamical Optimization of Consensus Laplacian Matrix

Autori: Senwen Zhan Hao Jiang Dong Shen and Wai-Ki Ching

Zdroj: East Asian Journal on Applied Mathematics. 0:0-0

Multi-view Data Clustering Through Consensus Graph and Data Representation Learning

Autori: Fadi Dornaika Sally El Hajjar

Zdroj: Advances in Data Clustering ISBN: 9789819776788

Correntropy based Low-rank Matrix Factorization with Multi-view Constraint Graph Learning for Multi-view Data Clustering

Autori: Nan Zhou Yuanhua Du Pan Chen a ďalší

Multi-view data clustering via non-negative matrix factorization with manifold regularization

Autori: Ghufran Ahmad Khan Jie Hu Tianrui Li a ďalší

Zdroj: International Journal of Machine Learning and Cybernetics. 13:677-689

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://link.springer.com/article/10.1007/s13042-021-01307-7

A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Autori: Luong, Thi Ngoc Khanh Nayak, Richi

Zdroj: 2020 IEEE 36th International Conference on Data Engineering (ICDE). :865-876

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Popis súboru: application/pdf

Prístupová URL adresa: https://eprints.qut.edu.au/200582/1/2CMV_ICDE2020_KhanhLuong.pdf
https://ieeexplore.ieee.org/document/9101762/
https://dblp.uni-trier.de/db/conf/icde/icde2020.html#LuongN20
https://eprints.qut.edu.au/200582/
https://www.computer.org/csdl/proceedings-article/icde/2020/09101762/1kaMFf5tkc0
https://doi.org/10.1109/ICDE48307.2020.00080

Semi-Supervised Learning and Feature Fusion for Multi-view Data Clustering

Autori: Justin Zhan Hadi Salman

Zdroj: 2020 IEEE International Conference on Big Data (Big Data). :645-650

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2020.html#SalmanZ20

Multi-View Robust Feature Learning for Data Clustering

Autori: Liang Zhao Tianyang Zhao Tingting Sun a ďalší

Zdroj: IEEE Signal Processing Letters. 27:1750-1754

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://ui.adsabs.harvard.edu/abs/2020ISPL...27.1750Z/abstract
https://ieeexplore.ieee.org/document/9206140
https://dblp.uni-trier.de/db/journals/spl/spl27.html#ZhaoZSLC20

Three-Layer Data Clustering Model for Multi-View Customer Segmentation using K-Means

Autori: Afgan Fazri Handoko Antoni Wibowo

Zdroj: International Journal of Recent Technology and Engineering (IJRTE). 8:1840-1846

Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization

Autori: Ghufran Ahmad Khan Jie Hu Qianqian Huang a ďalší

Zdroj: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). :1159-1165

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/iske/iske2019.html#Khan0LDH19