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Research Article | Open Access

PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering

Department of Engineering, University of Fukui, Fukui-shi 910-8507, Japan
Faculty of Science and Engineering, Iwate University,Morioka-shi 020-8551, Japan
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Graphical Abstract

Abstract

Subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques. However, when dealing with large datasets, representation of each data point by referring to all data points via a dictionary suffers from high computational complexity. To alleviate this issue, we introduce a parallelizable multi-subset based self-expressive model (PMS) which represents each data point by combining multiple subsets, with each consisting of only a small proportion of the samples. The adoption of PMS in subspace clustering (PMSSC) leads to computational advantages because the optimization problems decomposed over each subset are small, and can be solved efficiently in parallel. Furthermore, PMSSC is able to combine multiple self-expressive coefficient vectors obtained from subsets, which contributes to an improvement in self-expressiveness. Extensive experiments on synthetic and real-world datasets show the efficiency and effectiveness of our approach in comparison to other methods.

Keywords

subspace clusteringself-expressive modelbig datasubsetting

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Computational Visual Media
Volume 9 Issue 3,
September 2023
Pages 479-494
DOI: 10.1007/s41095-022-0293-5
Cite this article:
Hotta K, Akashi T, Tokai S, et al. PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering. Computational Visual Media, 2023, 9(3): 479-494. https://doi.org/10.1007/s41095-022-0293-5

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Received: 14 February 2022
Accepted: 08 May 2022
Published: 31 March 2023
© The Author(s) 2023.

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