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Advances in Principal Component Analysis

Research and Development

  • Ganesh R. Naik

Table of contents

  1. Front Matter
    Pages i-vii
  2. Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu
    Pages 1-18
  3. Aloke Datta, Susmita Ghosh, Ashish Ghosh
    Pages 19-46
  4. Marco Geraci, Alessio Farcomeni
    Pages 47-70
  5. Jiyong Oh, Nojun Kwak
    Pages 71-98
  6. Salaheddin Alakkari, John Dingliana
    Pages 99-120
  7. Panos P. Markopoulos, Sandipan Kundu, Shubham Chamadia, Nicholas Tsagkarakis, Dimitris A. Pados
    Pages 121-135
  8. Meng Lu, Kai He, Jianhua Z. Huang, Xiaoning Qian
    Pages 193-223
  9. Yannick Deville, Charlotte Revel, Véronique Achard, Xavier Briottet
    Pages 225-252

About this book

Introduction

This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.

Keywords

Principal Component Analysis (PCA) Source separation Source identification Dimensionality reduction Nonlinear PCA Kernel PCA Sparse PCA Time-frequency signal Pattern recognition

Editors and affiliations

  • Ganesh R. Naik
    • 1
  1. 1.BENS Research Group, MARCS InstituteWestern Sydney UniversityKingswoodAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-6704-4
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-10-6703-7
  • Online ISBN 978-981-10-6704-4
  • Buy this book on publisher's site