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Game theory based Bi-domanial deep subspace clustering. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Wang S, Chen Y, Yi S, Chao G. Frobenius norm-regularized robust graph learning for multi-view subspace clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03816-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Chen C, Lu H, Wei H, Geng X. Deep subspace image clustering network with self-expression and self-supervision. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03654-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Robust Spectral Clustering via Low-Rank Sample Representation. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/7540956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data’s self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coefficient of two samples, which are similar in original high-dimensional space, is close to maintaining the samples’ neighborhood structure in the low-dimensional space. As a result, the proposed method obtains a clustering structure directly through the low-rank coefficient matrix to guarantee optimal clustering performance. A wide range of experiments shows that the proposed method is superior to compared state-of-the-art methods.
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Clean and robust affinity matrix learning for multi-view clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03146-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Joint learning affinity matrix and representation matrix for robust low-rank multi-kernel clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02974-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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