Multiview Consensus Graph Clustering

Zhan, Kun; Nie, Feiping; Wang, Jing; Yang, Yi

doi:10.1109/tip.2018.2877335

Cited by 405 publications

(97 citation statements)

References 38 publications

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“…Nonetheless, [38,39] are equally two-phase methods. Moreover, the previous method [26] and the more recent one [40] can obtain a clustering structure directly without applying spectral clustering over the consensus similarity matrix. ey both achieved that by imposing a rank constraint on the Laplacian matrix to guarantee c exact number of connected components.…”

Section: Related Workmentioning

confidence: 99%

“…However, unlike these methods, our method learns a common low-rank representation matrix on only one instance of the data. en, following a similar approach in [26,40], we directly pursue a block diagonal clustering structure without performing any spectral postprocessing.…”

Section: Related Workmentioning

confidence: 99%

“…e reason is that different instances of cross-domain and multiview datasets often share a common property that is useful in the overall clustering task [9,10]. As such, several correlation learning techniques, including canonical correlation analysis (CCA) [11][12][13][14][15], cotraining [15][16][17], nonnegative matrix factorization [18,19], and subspace clustering [20][21][22][23][24][25][26] were utilized over the years to find a common representation in cross-domain and multiview datasets. For example, CCA [11] learns the linear relations between two sets of correlated variables with the nonlinear type proposed in [12].…”

Section: Introductionmentioning

confidence: 99%

“…erefore, the recent work in [26], which extends [25], attempts to resolve the above issue by allowing a common similarity matrix to approximate different view low embedding matrices.. However, the state-of-the-art method fails to consider the proximity issue mentioned above in constructing the individual view structure.…”

Section: Introductionmentioning

confidence: 99%

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Low Rank Correlation Representation and Clustering

Gao

Dai²,

Abhadiomhen

et al. 2021

Scientific Programming

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Correlation learning is a technique utilized to find a common representation in cross-domain and multiview datasets. However, most existing methods are not robust enough to handle noisy data. As such, the common representation matrix learned could be influenced easily by noisy samples inherent in different instances of the data. In this paper, we propose a novel correlation learning method based on a low-rank representation, which learns a common representation between two instances of data in a latent subspace. Specifically, we begin by learning a low-rank representation matrix and an orthogonal rotation matrix to handle the noisy samples in one instance of the data so that a second instance of the data can linearly reconstruct the low-rank representation. Our method then finds a similarity matrix that approximates the common low-rank representation matrix much better such that a rank constraint on the Laplacian matrix would reveal the clustering structure explicitly without any spectral postprocessing. Extensive experimental results on ORL, Yale, Coil-20, Caltech 101-20, and UCI digits datasets demonstrate that our method has superior performance than other state-of-the-art compared methods in six evaluation metrics.

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