Spectral Methods for Data Science : A Statistical Perspective

Yuxin Chen

ISBN 10: 1680838962 / ISBN 13: 9781680838961

Verlag: Now Publishers Inc, 2021

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9781680838961

Bibliografische Details

• Titel: Spectral Methods for Data Science : A Statistical Perspective
• Verlag: Now Publishers Inc
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1680838962
• ISBN-13: 9781680838961
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.