Consensus Multi-view Subspace Clustering in One Step

PROJECT TITLE :

Consensus One-step Multi-view Subspace Clustering

ABSTRACT:

The communities of multimedia, Machine Learning, and Data Mining are showing a growing interest in multi-view clustering. Due to its powerful ability to reveal the inherent low dimensional clustering structure that is hidden across views, multi-view subspace clustering (MVSC), which is one kind of the essential multi-view clustering algorithm, is becoming more and more popular. This is one of the reasons why. Existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices, which we observe; and these methods isolate the processes of affinity learning, multi-view information fusion, and clustering. This is true despite the fact that existing MVSC methods perform superiorly when it comes to clustering in a variety of applications. Both of these factors may contribute to an insufficient utilization of multi-view information, which ultimately results in an unsatisfactory performance of clustering. The purpose of this paper is to address these issues by proposing a novel method known as consensus one-step multi-view subspace clustering (COMVSC). Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful in removing noise from data. This is done in place of the traditional method of directly fusing multiple affinity matrices. A unified framework is used to simultaneously learn the affinity matrices, the consensus representation, and the final clustering labels matrix. Because of this, the three steps are able to negotiate with one another to determine how they can best serve the clustering task, which ultimately leads to improved performance. As a result, in order to solve the optimization problem that was caused by this, we propose using an iterative algorithm. Extensive experiment results on benchmark datasets show that our method is superior to other state-of-the-art approaches, as demonstrated by the results of the experiments.

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