Atnaujintas knygų su minimaliais defektais pasiūlymas! Naršykite ČIA >>
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.
| Autorius: | Yuxin Chen, Yuejie Chi, Jianqing Fan, |
| Leidėjas: | Now Publishers Inc |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 256 |
| ISBN-10: | 1680838962 |
| ISBN-13: | 9781680838961 |
| Formatas: | 234 x 156 x 14 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Spectral Methods for Data Science: A Statistical Perspective“
