Tensorized Anchor Graph Learning for Large-scale Multi-view Clustering

Dai, Jian; Ren, Zhenwen; Luo, Yunzhi; Song, Hong; Yang, Jian

doi:10.1007/s12559-023-10146-3

Cited by 2 publications

(1 citation statement)

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“…The learned consensus matrix contains not only consensus information across views, but also localized information between samples and anchors. Dai et al (2023) proposed to factorize the projected multi-view data based on anchors and constructed the third-order tensor as a regularization term using each view's subspace matrix. Unlike learning the consensus matrix of each view, the proposed method mines the view complementarity information through the viewspecific subspace matrix.…”

Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

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The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

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Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%