Graduate School and Research Center in Digital Sciences

A subspace co-training framework for multi-view clustering

Zhao, Xuran; Nicholas Evans; Dugelay, Jean-Luc

Pattern Recognition Letters, Elsevier, 8 December 2013

This paper addresses the problem of unsupervised clustering with multi-view data of high dimensionality. We propose a new algorithm which learns discriminative subspaces in an unsupervised fashion based upon the assumption that a reliable clustering should assign same-class samples to the same cluster in each view. The framework combines the simplicity of k-means clustering and Linear Discriminant Analysis (LDA) within a co-training scheme which exploits labels learned automatically in one view to learn discriminative subspaces in another. The effectiveness of the proposed algorithm is demonstrated empirically under scenarios where the conditional independence assumption is either fully satisfied (audio-visual speaker clustering) or only partially satisfied (handwritten digit clustering and document clustering). Significant improvements over alternative multi-view clustering approaches are reported in both cases. The new algorithm is flexible and can be readily adapted to use different distance measures, semi-supervised learning, and non-linear problems.

Document Doi Bibtex

Title:A subspace co-training framework for multi-view clustering
Keywords:Multi-view clustering; Subspace clustering; Co-training
Type:Journal
Language:English
City:
Date:
Department:Digital Security
Eurecom ref:4190
Copyright: © Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in Pattern Recognition Letters, Elsevier, 8 December 2013 and is available at : http://dx.doi.org/10.1016/j.patrec.2013.12.003
Bibtex: @article{EURECOM+4190, doi = {http://dx.doi.org/10.1016/j.patrec.2013.12.003}, year = {2013}, month = {12}, title = {{A} subspace co-training framework for multi-view clustering}, author = {{Z}hao, {X}uran and {N}icholas {E}vans and {D}ugelay, {J}ean-{L}uc}, journal = {{P}attern {R}ecognition {L}etters, {E}lsevier, 8 {D}ecember 2013}, url = {http://www.eurecom.fr/publication/4190} }
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