Self-Weighted Multi-View Clustering with Deep Matrix Factorization

Beilei Cui, Hong Yu, Tiantian Zhang, Siwen Li
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:567-582, 2019.

Abstract

Due to the efficiency of exploring multiple views of the real-word data, Multi-View Clustering (MVC) has attracted extensive attention from the scholars and researches based on it have made significant progress. However, multi-view data with numerous complementary information is vulnerable to various factors (such as noise). So it is an important and challenging task to discover the intrinsic characteristics hidden deeply in the data. In this paper, we present a novel MVC algorithm based on deep matrix factorization, named Self-Weighted Multi-view Clustering with Deep Matrix Factorization (SMDMF). By performing the deep decomposition structure, SMDMF can eliminate interference and reveal semantic information of the multi-view data. To properly integrate the complementary information among views, it assigns an automatic weight for each view without introducing supernumerary parameters. We also analyze the convergence of the algorithm and discuss the hierarchical parameters. The experimental results on four datasets show our algorithm is superior to other comparisons in all aspects.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-cui19a, title = {Self-Weighted Multi-View Clustering with Deep Matrix Factorization}, author = {Cui, Beilei and Yu, Hong and Zhang, Tiantian and Li, Siwen}, pages = {567--582}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/cui19a/cui19a.pdf}, url = {http://proceedings.mlr.press/v101/cui19a.html}, abstract = {Due to the efficiency of exploring multiple views of the real-word data, Multi-View Clustering (MVC) has attracted extensive attention from the scholars and researches based on it have made significant progress. However, multi-view data with numerous complementary information is vulnerable to various factors (such as noise). So it is an important and challenging task to discover the intrinsic characteristics hidden deeply in the data. In this paper, we present a novel MVC algorithm based on deep matrix factorization, named Self-Weighted Multi-view Clustering with Deep Matrix Factorization (SMDMF). By performing the deep decomposition structure, SMDMF can eliminate interference and reveal semantic information of the multi-view data. To properly integrate the complementary information among views, it assigns an automatic weight for each view without introducing supernumerary parameters. We also analyze the convergence of the algorithm and discuss the hierarchical parameters. The experimental results on four datasets show our algorithm is superior to other comparisons in all aspects.} }
Endnote
%0 Conference Paper %T Self-Weighted Multi-View Clustering with Deep Matrix Factorization %A Beilei Cui %A Hong Yu %A Tiantian Zhang %A Siwen Li %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-cui19a %I PMLR %J Proceedings of Machine Learning Research %P 567--582 %U http://proceedings.mlr.press %V 101 %W PMLR %X Due to the efficiency of exploring multiple views of the real-word data, Multi-View Clustering (MVC) has attracted extensive attention from the scholars and researches based on it have made significant progress. However, multi-view data with numerous complementary information is vulnerable to various factors (such as noise). So it is an important and challenging task to discover the intrinsic characteristics hidden deeply in the data. In this paper, we present a novel MVC algorithm based on deep matrix factorization, named Self-Weighted Multi-view Clustering with Deep Matrix Factorization (SMDMF). By performing the deep decomposition structure, SMDMF can eliminate interference and reveal semantic information of the multi-view data. To properly integrate the complementary information among views, it assigns an automatic weight for each view without introducing supernumerary parameters. We also analyze the convergence of the algorithm and discuss the hierarchical parameters. The experimental results on four datasets show our algorithm is superior to other comparisons in all aspects.
APA
Cui, B., Yu, H., Zhang, T. & Li, S.. (2019). Self-Weighted Multi-View Clustering with Deep Matrix Factorization. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:567-582

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