Discriminative sparse subspace learning with manifold regularization.

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 2 , 1 -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. • Subspace learning and spectral graphs are combined into a unified learning framework. • The learned projection maps samples to the subspace with optimal class separation. • The manifold regularization framework fusing decision boundary and subspace learning. • The sample structure is analyzed by fusing multimanifold and submanifold information. [ABSTRACT FROM AUTHOR]