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We study the multimodal and mixmodal data-driven supervised structural sparse subspace learning problem in this paper, and present the &#x03B1;-structural regularization based hierarchical locality analysis (&#x03B1;-SRHLA) model. Unlike most existing sparse subspace learning models that merely constrain the cardinalities of the subspace basis vectors, the present &#x03B1;-SRHLA model takes into account the structural correlations of the original data variables and generates &#x201C;variable groups&#x201D;for sparse subspace learning. As a result, the sparsity is induced in the scale of the variable group instead of the single variable, i.e., &#x201C;structural sparsity&#x201D;. In addition, the &#x03B1;-SRHLA considers the &#x201C;hierarchical locality&#x201D;of multimodal and mixmodal data, and derives the weighted local affinity correlations in both data-level and class-level. This helps to reveal the intrinsic distribution characteristics of the considered multimodal and mixmodal manifold structures. A series of experiments on normal and multimodal data classification, multimodal and mixmodal digit as well as face recognition verify the effectiveness of the present &#x03B1;-SRHLA model in dealing with both multimodal and mixmodal data.

Supervised Structural Sparse Subspace Learning Based on Hierarchical Locality Preservation for Multimodal and Mixmodal Data