title: LGE-KSVD: robust sparse representation classification.
abstract: The parsimonious nature of sparse representations has been successfully exploited for the development of highly accurate classifiers for various scientific applications. Despite the successes of Sparse Representation techniques, a large number of dictionary atoms as well as the high dimensionality of the data can make these classifiers computationally demanding. Furthermore, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where, for example, variations in pose may affect identity and expression recognition. We analyze the interaction between dimensionality reduction and sparse representations, and propose a technique, called Linear extension of Graph Embedding K-means-based Singular Value Decomposition (LGE-KSVD) to address both issues of computational intensity and coefficient contamination. In particular, the LGE-KSVD utilizes variants of the LGE to optimize the K-SVD, an iterative technique for small yet over complete dictionary learning. The dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier are jointly learned through the LGE-KSVD. The atom optimization process is redefined to allow variable support using graph embedding techniques and produce a more flexible and elegant dictionary learning algorithm. Results are presented on a wide variety of facial and activity recognition problems that demonstrate the robustness of the proposed method.
authors.forename: Raymond
authors.initials: R
authors.lastname: Ptucha
authors.forename: Andreas E
authors.initials: AE
authors.lastname: Savakis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*