Principal Component Analysis Networks and Algorithms

Xiangyu Kong, Changhua Hu, Zhansheng Duan

  • 出版商: Springer
  • 出版日期: 2017-01-13
  • 售價: $6,580
  • 貴賓價: 9.5$6,251
  • 語言: 英文
  • 頁數: 323
  • 裝訂: Hardcover
  • ISBN: 981102913X
  • ISBN-13: 9789811029134
  • 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)

這本書不僅提供了對控制科學中基於神經的主成分分析(PCA)方法的全面介紹,還介紹了許多新穎的PCA算法及其擴展和推廣,例如雙重目的、耦合PCA、GED、基於神經的SVD算法等。它還詳細討論了各種分析方法,用於分析算法的收斂性、穩定性和自穩定性,並介紹了用於分析PCA/MCA算法收斂性的確定性離散時間系統方法。讀者應該熟悉數值分析和統計學的基礎知識,例如最小二乘法和隨機算法的基礎。儘管它專注於神經網絡,但本書僅介紹了它們的學習法則,即一種簡單的迭代算法。因此,不需要對神經網絡有先驗知識。這本書將對應用數學、統計學、工程學和其他相關領域的研究人員和學生具有興趣,並可作為參考資料。