中图分类法:	TP181 版次：
著者:	Wright, John
题名:	High-dimensional data analysis with low-dimensional models : [ principles, computation, and applications /] / ,
出版发行:	出版地： Cambridge : 出版社： Cambridge University Press, 出版日期： 2022.
载体形态:	xxx, 685 pages : illustrations ; 25 cm
内容提要:	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 Cande.
主题词:	Machine learning Mathematical models.
主题词:	Big data Mathematical models.
主要责任者:	Ma, Yi, Ma, Yi,