Spectral clustering with distinction and consensus learning on multiple views data.

Peng Zhou,Fan Ye,Liang Du,Ivan Olier

doi:10.1371/journal.pone.0208494

Abstract

Since multi-view data are available in many real-world clustering problems, multi-view clustering has received considerable attention in recent years. Most existing multi-view clustering methods learn consensus clustering results but do not make full use of the distinct knowledge in each view so that they cannot well guarantee the complementarity across different views. In this paper, we propose a Distinction based Consensus Spectral Clustering (DCSC), which not only learns a consensus result of clustering, but also explicitly captures the distinct variance of each view. It is by using the distinct variance of each view that DCSC can learn a clearer consensus clustering result. In order to optimize the introduced optimization problem effectively, we develop a block coordinate descent algorithm which is theoretically guaranteed to converge. Experimental results on real-world data sets demonstrate the effectiveness of our method.

Highlights

Many real-world data sets are represented in multiple views
We highlight the main contribution of this paper here: we propose a new multiview spectral clustering method, which uses the Hilbert Schmidt Independence Criterion (HSIC) to explicitly capture the distinction information of all views and can obtain a clearer and more accurate consensus result; and Spectral clustering with distinction and consensus learning on multiple views data we provide a block coordinate descent algorithm to solve it effectively and the experimental results demonstrate that our algorithm outperforms other state-of-the-art methods
Since we explicitly capture the distinct variances of all views by minimizing the dependency among them, the remainder is a clearer consensus spectral embedding leading to a better clustering result

Summary

Introduction

Images on the web may have two views: visual information and textual tags; multi-lingual data sets have multiple representations in different languages. They aim to learn a common or consensus clustering result from multiple views. These methods [1,2,3,4,5,6] usually extend single view clustering methods such as spectral clustering or nonnegative matrix factorization (NMF) to deal with multi-view data. Kumar et al proposed two coregularization based approaches for multi-view spectral clustering by enforcing the clustering hypotheses on different views to agree with each other [4].

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