Connecting theory with practice, this systematic and rigorous
introduction covers the fundamental principles, algorithms and
applications of key mathematical models for high-dimensional data
analysis. Comprehensive in its approach, it provides unified coverage of
many different low-dimensional models and analytical techniques,
including sparse and low-rank models, and both convex and non-convex
formulations. Readers will learn how to develop efficient and scalable
algorithms for solving real-world problems, supported by numerous
examples and exercises throughout, and how to use the computational
tools learnt in several application contexts. Applications presented
include scientific imaging, communication, face recognition, 3D vision,
and deep networks for classification. With code available online, this
is an ideal textbook for senior and graduate students in computer
science, data science, and electrical engineering, as well as for those
taking courses on sparsity, low-dimensional structures, and
high-dimensional data. Foreword by Emmanuel Candès.