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Neural Processing Letters

Erschienen in:

09.08.2021

Subspace Clustering via Integrating Sparse Representation and Adaptive Graph Learning

verfasst von: Zhiyang Gu, Zhenghong Deng, Yijie Huang, De Liu, Zhan Zhang

Erschienen in: Neural Processing Letters | Ausgabe 6/2021

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Abstract

Sparse representation is a powerful tool for subspace clustering, but most existing methods for this issue ignore the local manifold information in learning procedure. To this end, in this paper we propose a novel model, dubbed Sparse Representation with Adaptive Graph (SRAG), which integrates adaptive graph learning and sparse representation into a unified framework. Specifically, the former can preserve the local manifold structure of data, while the latter is useful for digging global information. For the objective function of SRAG has multiple intractable terms, an ADMM method is developed to solve it. Numerous experimental results demonstrate that our proposed method consistently outperforms several representative clustering algorithms by significant margins.

Vorheriger Artikel A New Result on Stability Analysis of Recurrent Neural Networks with Time-Varying Delay Based on an Extended Delay-Dependent Integral Inequality

Nächster Artikel Input-to-State Stability for Stochastic Delay Neural Networks with Markovian Switching

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Titel: Subspace Clustering via Integrating Sparse Representation and Adaptive Graph Learning
verfasst von: Zhiyang Gu
Zhenghong Deng
Yijie Huang
De Liu
Zhan Zhang
Publikationsdatum: 09.08.2021
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 6/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-021-10603-w

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