Spectral Methods for Data Science - A Statistical Perspective (Paperback)

In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. A diverse array of applications have been found in machine learning, imaging science, financial and econometric modeling, and signal processing.This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large-scale applications. The authors provide a unified and comprehensive treatment that establishes the theoretical underpinnings for spectral methods, particularly through a statistical lens.Building on years of research experience in the field, the authors present a powerful framework, called leave-one-out analysis, that proves effective and versatile for delivering fine-grained performance guarantees for a variety of problems. This book is essential reading for all students, researchers and practitioners working in Data Science.

General

Imprint:	Now Publishers Inc
Country of origin:	United States
Series:	Foundations and Trends (R) in Machine Learning
Release date:	October 2021
First published:	2021
Authors:	Yuxin Chen • Yuejie Chi • Jianqing Fan • Cong Ma
Dimensions:	234 x 156mm (L x W)
Format:	Paperback
Pages:	254
ISBN-13:	978-1-68083-896-1
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-68083-896-2
Barcode:	9781680838961

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