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

This textbook connects theory with practice, covering fundamental principles, scalable algorithms, and real-world applications of mathematical models like sparse and low-rank structures for high-dimensional data.

It addresses convex and non-convex optimization techniques, with examples in scientific imaging, face recognition, 3D vision, and deep networks.

Designed for senior and graduate students in computer science, data science, and electrical engineering, it includes exercises and online code resources.