This unique text reference presents a comprehensive review of the state of the art in sparse
representations modeling and learning. The book examines both the theoretical foundations and
details of algorithm implementation highlighting the practical application of compressed
sensing research in visual recognition and computer vision. Topics and features: describes
sparse recovery approaches robust and efficient sparse representation and large-scale visual
recognition covers feature representation and learning sparsity induced similarity and
sparse representation and learning-based classifiers discusses low-rank matrix approximation
graphical models in compressed sensing collaborative representation-based classification and
high-dimensional nonlinear learning includes appendices outlining additional computer
programming resources and explaining the essential mathematics required to understand the
book.
