High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

Livro digital

Título:
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

Autor:
John Wright, Yi Ma

Categoria:
Tecnologia > Geral

Doador:
Raffaello D. N.

Sinopse:
High-dimensional data often looks unmanageable until you find the right low-dimensional structure hiding inside it, and this book starts by making that promise concrete through a chapter sequence that moves from sparse signal models to convex methods for sparse recovery, then to low-rank matrix recovery, optimization algorithms, and finally major applications. John Wright and Yi Ma build the subject around sparsity and low rank, showing how these models support recovery from incomplete or noisy measurements, why l1 minimization works, and how phase transitions and restricted isometry shape success. The table of contents also reveals practical depth, with chapters on matrix recovery, robust principal component analysis, and efficient algorithms for large-scale convex and nonconvex optimization. The result is an advanced guide that connects mathematical ideas to medical imaging, face recognition, recommendation systems, wireless sensing, and computer vision. Readers get a strong blend of theory, computation, and applications, plus a clear view of how modern data-analysis methods are designed, analyzed, and used in practice.