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Your search keyword '"Li, Xuelong"' showing total 2 results
2 results on '"Li, Xuelong"'

1. Unsupervised Feature Selection With Extended OLSDA via Embedding Nonnegative Manifold Structure.

Author

Zhang, Rui, Zhang, Hongyuan, Li, Xuelong, and Yang, Sheng

Subjects
LAPLACIAN matrices, FEATURE selection, DISCRIMINANT analysis, LEAST squares, SPARSE matrices, LINEAR programming
Abstract

As to unsupervised learning, most discriminative information is encoded in the cluster labels. To obtain the pseudo labels, unsupervised feature selection methods usually utilize spectral clustering to generate them. Nonetheless, two related disadvantages exist accordingly: 1) the performance of feature selection highly depends on the constructed Laplacian matrix and 2) the pseudo labels are obtained with mixed signs, while the real ones should be nonnegative. To address this problem, a novel approach for unsupervised feature selection is proposed by extending orthogonal least square discriminant analysis (OLSDA) to the unsupervised case, such that nonnegative pseudo labels can be achieved. Additionally, an orthogonal constraint is imposed on the class indicator to hold the manifold structure. Furthermore, $\ell _{2,1}$ regularization is imposed to ensure that the projection matrix is row sparse for efficient feature selection and proved to be equivalent to $\ell _{2,0}$ regularization. Finally, extensive experiments on nine benchmark data sets are conducted to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Robust Bi-Stochastic Graph Regularized Matrix Factorization for Data Clustering.

Author

Wang, Qi, He, Xiang, Jiang, Xu, and Li, Xuelong

Subjects
MATRIX decomposition, FACTORIZATION, NONNEGATIVE matrices, SPARSE matrices, STOCHASTIC matrices, TASK analysis
Abstract

Data clustering, which is to partition the given data into different groups, has attracted much attention. Recently various effective algorithms have been developed to tackle the task. Among these methods, non-negative matrix factorization (NMF) has been demonstrated to be a powerful tool. However, there are still some problems. First, the standard NMF is sensitive to noises and outliers. Although $\ell _{2,1}$ ℓ 2 , 1 norm based NMF improves the robustness, it is still affected easily by large noises. Second, for most graph regularized NMF, the performance highly depends on the initial similarity graph. Third, many graph-based NMF models perform the graph construction and matrix factorization in two separated steps. Thus the learned graph structure may not be optimal. To overcome the above drawbacks, we propose a robust bi-stochastic graph regularized matrix factorization (RBSMF) framework for data clustering. Specifically, we present a general loss function, which is more robust than the commonly used $L_2$ L 2 and $L_1$ L 1 functions. Besides, instead of keeping the graph fixed, we learn an adaptive similarity graph. Furthermore, the graph updating and matrix factorization are processed simultaneously, which can make the learned graph more appropriate for clustering. Extensive experiments have shown the proposed RBSMF outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2022
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