Manifold Regularized Multi-view Subspace Clustering for image representation

Lei Wang, Danping Li, Tiancheng He, Zhong Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Subspace clustering refers to the task of clustering a collection of points drawn from a high-dimensional space into a union of multiple subspaces that best fits them. State-of-the-art approaches have been proposed for tackling this clustering problem by using the low-rank or sparse optimization techniques. However, most of the traditional subspace clustering methods are developed for single-view data and are not directly applicable to the multi-view scenario. In this paper, we present a Manifold Regularized Multi-view Subspace Clustering (MRMSC) method to better incorporate the correlated and complementary information from different views. MRMSC yields a unified affinity representation by joint optimization across different views. To respect the data manifold locally, the graph Laplacian is constructed to maintain the intrinsic geometrical structure of each view. In the multi-view integration, a sparsity constraint is imposed to the unified affinity representation in order to better reflect the data relationship from multiple views or features. In experiments, we compared the performance of clustering using MRMSC with the single-view and concatenate-multi-view methods on different datasets. The results showed that better clustering performance can be achieved by fusing the multiple features with a unified affinity representation by MRMSC.

Original languageEnglish (US)
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-288
Number of pages6
ISBN (Electronic)9781509048472
DOIs
StatePublished - Jan 1 2016
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: Dec 4 2016Dec 8 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume0
ISSN (Print)1051-4651

Other

Other23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period12/4/1612/8/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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