Principal Component Analysis

Sanguansat, Parinya

Principal Component Analysis

Edited by Parinya Sanguansat

Abstract: This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.

JEL-codes: C60 (search for similar items in EconPapers)
Date: 2012
ISBN: 978-953-51-0195-6
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Citations: View citations in EconPapers (2)

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Book downloadable chapter-by-chapter

Chapters in this book:

Acceleration of Convergence of the Alternating Least Squares Algorithm for Nonlinear Principal Components Analysis: Masahiro Kuroda
Application of Linear and Nonlinear Dimensionality Reduction Methods: Ramana Kumar Vinjamuri, Mingui Sun, Zhi-Hong Mao and Wei Wang
Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information: Cuauhtemoc Araujo Andrade, Claudio Frausto-Reyes, Esteban Gerbino, Pablo Mobili, Elizabeth Tymczyszyn, Edgar L. Esparza Ibarra, Rumen Ivanov-Tsonchev and Andrea Gomez-Zavaglia
Computing and Updating Principal Components of Discrete and Continuous Point Sets: Darko Dimitrov
FPGA Implementation for GHA-Based Texture Classification: Shiow-Jyu Lin, Kun-Hung Lin and Wen-Jyi Hwang
Multilinear Supervised Neighborhood Preserving Embedding Analysis of Local Descriptor Tensor: Xian-Hua Han
On-Line Monitoring of Batch Process with Multiway PCA/ICA: Xiang Gao
Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology: Maria Monfreda
Robust Density Comparison Using Eigenvalue Decomposition: Omar Arif and Patricio A. Vela
Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis: Charles Guyon, Thierry Bouwmans and El-Hadi Zahzah
Subset Basis Approximation of Kernel Principal Component Analysis: Yoshikazu Washizawa
The Basics of Linear Principal Components Analysis: Yaya Keho
The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance: Mauridhi Hery Pumomo
Two-Dimensional Principal Component Analysis and Its Extensions: Parinya Sanguansat

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Persistent link: https://EconPapers.repec.org/RePEc:ito:pbooks:1825

DOI: 10.5772/2340

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