Robust Multi-view Subspace Learning Through Structured Low-Rank Matrix Recovery

Xu, Jiamiao; You, Xinge; Zheng, Qi; Wang, Fangzhao; Zhang, Peng

doi:10.1007/978-3-030-03338-5_36

Cited by 1 publication

(2 citation statements)

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“…The main idea is to utilize these dictionaries to explore the correlated information and the complementary information simultaneously to find a robust face representation that will enhance face recognition. In [28], Xu et al proposed a structured low-rank matrix recovery method to solve the problem of huge divergence in multi-view face data. This approach eliminates graph embedding by introducing a class label matrix to learn a discriminative unified matrix.…”

Section: Related Workmentioning

confidence: 99%

“…In [31], Meng et al proposed a Multi-View Low-Rank Preserving Embedding (MvLPE) method, which learns the complimentary features in different views by maximizing the agreement between an individual view and a centroid view. Nonetheless, these methods equally ignored the specific local structure of different views as [28]. Therefore, inspired by [17], we utilize the L1 norm to capture the specific local structure of each view such that a common face representation learned is through a cooperative effort to guarantee a more optimum solution that suppresses the effect of noisy data adaptably.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐view intrinsic low‐rank representation for robust face recognition and clustering

Wang

Abhadiomhen

Liu

et al. 2021

IET Image Processing

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In the last years, subspace-based multi-view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi-view low-rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low-rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low-rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low-rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state-of-the-art methods in classification and clustering. How to cite this article: Wang, Z.-y., et al.: Multi-view intrinsic low-rank representation for robust face recognition and clustering. IET Image Process. 1-12 (2021).

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Section: Related Workmentioning

confidence: 99%