Spectral Methods for Data Science: A Statistical Perspective

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.

Author: Yuxin Chen, Yuejie Chi, Jianqing Fan
Publisher: Now Publishers
Published: 10/21/2021
Pages: 256
Binding Type: Paperback
Weight: 0.80lbs
Size: 9.21h x 6.14w x 0.54d
ISBN13: 9781680838961
ISBN10: 1680838962
BISAC Categories:
- Computers | Machine Theory
- Computers | Data Science | Machine Learning