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Pattern Analysis and Applications

Erschienen in:

16.09.2022 | Theoretical Advances

Latent block diagonal representation for subspace clustering

verfasst von: Jie Guo, Lai Wei

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2023

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Abstract

Spectral-type subspace clustering algorithms have attracted wide attention because of their excellent performance displayed in a great deal of applications in machine learning domain. It is critical for spectral-type subspace clustering algorithms to obtain suitable coefficient matrices which could reflect the subspace structures of data sets. In this paper, we propose a latent block diagonal representation clustering algorithm (LBDR). For a data set, the goal of LBDR is to construct a block diagonal and dense coefficient matrix and settle the noise adaptively within the original data set by using dimension reduction technique concurrently. In brief, by seeking the solution of a joint optimization problem, LBDR is capable of finding a suitable coefficient matrix and a projection matrix. Furthermore, a series of experiments conducted on several benchmark databases show that LBDR dominates the related methods.

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ABLBDR is a deep subspace clustering algorithm which is suitable for very high-dimensional data. And the dimension of data in Hopkin 155 is relatively low.

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Titel: Latent block diagonal representation for subspace clustering
verfasst von: Jie Guo
Lai Wei
Publikationsdatum: 16.09.2022
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-022-01101-3

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