Graph-based Multi-view Binary Learning for image clustering

Jiang, Guangqi; Wang, Huibing; Peng, Jinjia; Chen, Dongyan; Fu, Xianping

doi:10.1016/j.neucom.2020.07.132

Cited by 24 publications

(3 citation statements)

References 29 publications

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“…In order to overcome these limitations, we propose a novel weighted low-rank tensor representation (WLRTR) method, which uses Tucker decomposition to simplify the calculation of the tensor nuclear norm and assigns different weights to the core tensor to take advantage of the main information in different views. The proposed WLRTR is formulated as: 1) ; E (2) ; /; E (V) ,…”

Section: Model Formulationmentioning

confidence: 99%

“…Therefore, the Eq. 4 can be transformed into the following formulation: 1) ; E (2) ; /; E (V) , Z Y.…”

Section: Optimization Of Wlrtrmentioning

confidence: 99%

“…Multi-view features not only contain compatible information, but also provide complementary information, which boost the performance of data analysis. Recently, [1] applied multi-view binary learning to obtain the supplementary information from multiple views. [2] proposed a kernelized multi-view subspace analysis method via self-weighted learning.…”

Section: Introductionmentioning

confidence: 99%

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Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

2021

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Multi-view clustering has been deeply explored since the compatible and complementary information among views can be well captured. Recently, the low-rank tensor representation-based methods have effectively improved the clustering performance by exploring high-order correlations between multiple views. However, most of them often express the low-rank structure of the self-representative tensor by the sum of unfolded matrix nuclear norms, which may cause the loss of information in the tensor structure. In addition, the amount of effective information in all views is not consistent, and it is unreasonable to treat their contribution to clustering equally. To address the above issues, we propose a novel weighted low-rank tensor representation (WLRTR) method for multi-view subspace clustering, which encodes the low-rank structure of the representation tensor through Tucker decomposition and weights the core tensor to retain the main information of the views. Under the augmented Lagrangian method framework, an iterative algorithm is designed to solve the WLRTR method. Numerical studies on four real databases have proved that WLRTR is superior to eight state-of-the-art clustering methods.

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Section: Model Formulationmentioning

confidence: 99%

“…Therefore, the Eq. 4 can be transformed into the following formulation: 1) ; E (2) ; /; E (V) , Z Y.…”

Section: Optimization Of Wlrtrmentioning

confidence: 99%