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Books in the "Matrix Factorization and Decomposition" category examine mathematical and computational techniques for expressing matrices (and higher-order tensors) as products or combinations of simpler components to reveal structure, reduce dimensionality, enable efficient computation, or extract interpretable features. They cover theoretical foundations in linear algebra and numerical analysis as well as core decompositions and factorizations—SVD/PCA, eigen- and spectral decompositions, LU/QR/Cholesky, nonnegative matrix factorization (NMF), CUR, and tensor decompositions (CP, Tucker)—and delve into algorithms, stability, optimization methods, and modern randomized and scalable approaches. Practical chapters typically illustrate applications in machine learning and recommender systems, signal and image processing, statistics, scientific computing, and data compression, with implementation guidance, performance considerations, and real-world case studies aimed at mathematicians, computer scientists, and practitioners seeking both theory and hands-on tools.

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