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

We study the multimodal and mixmodal data-driven supervised structural sparse subspace learning problem in this paper, and present the α-structural regularization based hierarchical locality analysis (α-SRHLA) model. Unlike most existing sparse subspace learning models that merely constrain the cardinalities of the subspace basis vectors, the present α-SRHLA model takes into account the structural correlations of the original data variables and generates “variable groups”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., “structural sparsity”. In addition, the α-SRHLA considers the “hierarchical locality”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 α-SRHLA model in dealing with both multimodal and mixmodal data.