Preserving bilateral view structural information for subspace clustering

Chong Peng; Jing Zhang; Yongyong Chen; Xin Xing; Chenglizhao Chen; Zhao Kang; Li Guo; Qiang Cheng

doi:10.1016/j.knosys.2022.109915

Preserving bilateral view structural information for subspace clustering

Chong Peng, Jing Zhang, Yongyong Chen, Xin Xing, Chenglizhao Chen, Zhao Kang, Li Guo, Qiang Cheng

Research output: Contribution to journal › Article › peer-review

Abstract

Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.

Original language	English
Article number	109915
Journal	Knowledge-Based Systems
Volume	258
DOIs	https://doi.org/10.1016/j.knosys.2022.109915
State	Published - Dec 22 2022

Bibliographical note

Funding Information:
This work was supported by in part by the National Natural Science Foundation of China (NSFC) under Grants 62172246 , 62106063 , 61802215 , and 61806045 ; in part by the Natural Science Foundation of Shandong Province under Grants ZR2019QF009 and ZR2019BF011 ; in part by the Guangdong Natural Science Foundation under Grant 2022A1515010819 ; in part by the Shenzhen College Stability Support Plan under Grant GXWD20201230155427003-20200824113231001 ; in part by the Shandong Province Colleges and Universities Youth Innovation Technology Plan Innovation Team Project under Grant 2021KJ062 and Grant 2020KJN011 ; in part by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under Grant 2022B1212010005 ; and in part by the National Institutes of Health (NIH) under Grants R21AG070909 and UH3 NS100606-03 .

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

Ridge regression
Structural information
Subspace clustering
Two-dimensional data

ASJC Scopus subject areas

Software
Management Information Systems
Information Systems and Management
Artificial Intelligence

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10.1016/j.knosys.2022.109915

Cite this

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title = "Preserving bilateral view structural information for subspace clustering",

abstract = "Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.",

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AU - Peng, Chong

AU - Zhang, Jing

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AU - Xing, Xin

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AU - Kang, Zhao

AU - Guo, Li

AU - Cheng, Qiang

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N2 - Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.

AB - Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.

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Preserving bilateral view structural information for subspace clustering

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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