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Discriminative sparse subspace learning with manifold regularization
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DOI：10.1016/j.eswa.2024.123831.png)
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摘要
En 中文
Common subspace learning methods only utilize local or global structure in feature extraction, and cannot obtain the global optimal discriminative projection matrix. For this reason, this paper proposes a discriminative sparse subspace learning method based on the manifold regularization framework (DSSL-MR), which introduces the graph Laplacian matrix that reflects the intrinsic geometric structure of the sample as a penalty term. DSSL-MR simultaneously uses both sub -manifold and multi -manifold information of samples for obtaining optimal projection to enhance the discriminability of different classes in subspace. DSSL-MR uses the sparse property of the L 21 -norm to constrain the projection matrix, which can eliminate redundant features and select features that are significant for classification. It is a linear supervised method, which belongs to the Fisher discriminant analysis framework. Experimental results on multiple real -world datasets show that the algorithm is very effective in classification and has high recognition rates.
Keyword:
Manifold regularization
Subspace learning
Linear classification
Sparse constraint
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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