Title: Review of multi-view subspace clustering

Authors: Jing Xia

Addresses: School of Computer Science and Technology, China University of Mining and Technology, China

Abstract: Multi-view subspace clustering is a type of subspace clustering which combines with multi-view learning. It cannot only deal with the challenges of the big data and high dimensions, but also cluster multi-view data from multiple sources and observation angles according to some similarity measurement. Our paper is to introduce the theoretical basis and latest research progress of multi-view subspace clustering. First, we briefly describe the basic principles, research status and classification of subspace clustering, and also compare several kinds of subspace clustering algorithms; next, multi-view subspace clustering is described in detail, and the ideas of several clustering algorithms are analysed and summarised. After that, we elaborate the research status of multi-view subspace clustering. Finally, the application of multi-view subspace clustering in various fields' application is introduced. The purpose of this paper is for beginners to quickly know about the research status of multi-view subspace clustering and some ideas of typical algorithms.

Keywords: multi-view subspace clustering; subspace clustering; data mining; self-representation; spectral clustering-based methods.

DOI: 10.1504/IJCI.2020.111671

International Journal of Collaborative Intelligence, 2020 Vol.2 No.2, pp.146 - 157

Received: 28 Apr 2020
Accepted: 25 May 2020

Published online: 08 Dec 2020 *

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