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

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 electrical engineering, computer science and data science, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.

這本系統性且嚴謹的介紹將理論與實踐相結合，涵蓋了高維數據分析的關鍵數學模型的基本原理、算法和應用。其方法全面，統一涵蓋了許多不同的低維模型和分析技術，包括稀疏和低秩模型，以及凸和非凸形式。讀者將學習如何開發高效且可擴展的算法來解決現實世界的問題，並通過豐富的例子和練習來支持學習計算工具在多個應用情境中的應用。所介紹的應用包括科學成像、通信、人臉識別、3D視覺和用於分類的深度網絡。本書附有在線代碼，是電氣工程、計算機科學和數據科學高年級和研究生學生的理想教材，也適用於修讀稀疏性、低維結構和高維數據課程的學生。Emmanuel Candès作序。