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"Sparse representations" as a book category covers the theory, methods, and applications of representing signals, data, or models using only a small number of active components from a larger set (a dictionary). Books in this category typically introduce the mathematical foundations (sparsity measures, l0/l1 norms, compressed sensing theory), optimization and algorithmic techniques (basis pursuit, LASSO, matching pursuit, orthogonal matching pursuit, greedy and convex methods), and practical topics such as dictionary learning, sparse coding, and sparse modeling for image processing, audio, neuroscience, statistics, and machine learning. They range from accessible introductions that emphasize intuition and applications to rigorous monographs focused on proof techniques, and often assume familiarity with linear algebra, probability, and optimization. Overall, the category appeals to researchers and practitioners interested in efficient, interpretable representations and modern data-acquisition or inference methods that exploit underlying sparsity.

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