In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.
Examples of topics which have developed from the advances of ICA, which are covered in the book are:
- A unifying probabilistic model for PCA and ICA
- Optimization methods for matrix decompositions
- Insights into the FastICA algorithm
- Unsupervised deep learning
- Machine vision and image retrieval
- A review of developments in the theory and applications of independent component analysis, and its influence in important areas such as statistical signal processing, pattern recognition and deep learning.
- A diverse set of application fields, ranging from machine vision to science policy data.
- Contributions from leading researchers in the field.
Author(s): Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen
Edition: 1
Publisher: Academic Press
Year: 2015
Language: English
Pages: 328
Tags: Приборостроение;Обработка сигналов;Статистические методы;
Content:
Front Matter, Pages i-ii
Copyright, Page iv
Preface, Page xiii, Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen Espoo
About the Editors, Page xv
List of Contributors, Pages xvii-xviii, Traian Abrudan, Ella Bingham, Guangyong Chen, KyungHyun Cho, Scott C. Douglas, Markku Hauta-Kasari, Pheng Ann Heng, Aapo Hyvärinen, Satoru Ishikawa, Heikki Kälviäinen, Juha Karhunen, Irwin King, Visa Koivunen, Zbyněk Koldovský, Markus Koskela, Jorma Laaksonen, Hannu Laamanen, Heikki Mannila, Jussi Parkkinen, Matti Pietikäinen, Tapani Raiko, et al.
Introduction, Pages xix-xxvi, Ricardo Vigário
Chapter Abstracts, Pages xxvii-xxxii
Chapter 1 - The initial convergence rate of the FastICA algorithm: The “One-Third Rule”, Pages 3-51, Scott C. Douglas
Chapter 2 - Improved variants of the FastICA algorithm, Pages 53-74, Zbynvěk Koldovský, Petr Tichavský
Chapter 3 - A unified probabilistic model for independent and principal component analysis, Pages 75-82, Aapo Hyvärinen
Chapter 4 - Riemannian optimization in complex-valued ICA, Pages 83-94, Visa Koivunen, Traian Abrudan
Chapter 5 - Nonadditive optimization, Pages 95-103, Zhirong Yang, Irwin King
Chapter 6 - Image denoising, local factor analysis, Bayesian Ying-Yang harmony learning1, Pages 105-124, Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng, Lei Xu
Chapter 7 - Unsupervised deep learning: A short review, Pages 125-142, Juha Karhunen, Tapani Raiko, KyungHyun Cho
Chapter 8 - From neural PCA to deep unsupervised learning, Pages 143-171, Harri Valpola
Chapter 9 - Two decades of local binary patterns: A survey, Pages 175-210, Matti Pietikäinen, Guoying Zhao
Chapter 10 - Subspace approach in spectral color science, Pages 211-221, Jussi Parkkinen, Hannu Laamanen, Markku Hauta-Kasari
Chapter 11 - From pattern recognition methods to machine vision applications, Pages 223-247, Heikki Kälviäinen
Chapter 12 - Advances in visual concept detection: Ten years of TRECVID, Pages 249-278, Ville Viitaniemi, Mats Sjöberg, Markus Koskela, Satoru Ishikawa, Jorma Laaksonen
Chapter 13 - On the applicability of latent variable modeling to research system data, Pages 279-288, Ella Bingham, Heikki Mannila
Index, Pages 289-296
