Principal Component Analysis Networks and Algorithms
暫譯: 主成分分析網絡與演算法
Xiangyu Kong, Changhua Hu, Zhansheng Duan
- 出版商: Springer
- 出版日期: 2017-01-13
- 售價: $6,780
- 貴賓價: 9.5 折 $6,441
- 語言: 英文
- 頁數: 323
- 裝訂: Hardcover
- ISBN: 981102913X
- ISBN-13: 9789811029134
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相關分類:
Algorithms-data-structures
海外代購書籍(需單獨結帳)
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商品描述
This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
商品描述(中文翻譯)
本書不僅提供了控制科學中基於神經網絡的主成分分析(PCA)方法的全面介紹,還介紹了許多新穎的PCA演算法及其擴展和一般化,例如雙重目的、耦合PCA、GED、基於神經網絡的奇異值分解(SVD)演算法等。它還詳細討論了各種分析方法,以研究演算法的收斂性、穩定性和自穩定性,並引入了確定性離散時間系統的方法來分析PCA/MCA演算法的收斂性。讀者應該熟悉數值分析和統計學的基本原理,例如最小二乘法和隨機演算法的基礎知識。雖然本書專注於神經網絡,但僅介紹其學習法則,這只是一種迭代演算法。因此,無需具備神經網絡的知識。本書將對應用數學、統計學、工程學及其他相關領域的研究人員和學生具有興趣,並作為參考資料。