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

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

Blog Article

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 ANABOLIC STATE PEACH MANGO 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 Physical and Digital Poster 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.