Advances in Independent Component Analysis and Learning Machines 1st Edition by Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen ISBN 0128028068 978-0128028063

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Product details: 

ISBN 10: 0128028068 

ISBN 13: 978-0128028063

Author: Ella Bingham, Samuel Kaski, Jorma Laaksonen, Jouko Lampinen

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

Table of contents: 

Part 1: Methods
The Initial Convergence Rate of the FastICA Algorithm: The “One-Third Rule”

Improved Variants of the FastICA Algorithm

A Unified Probabilistic Model for Independent and Principal Component Analysis

Riemannian Optimization in Complex-Valued ICA

Non-Additive Optimization

Image Denoising via Local Factor Analysis under the Bayesian Ying-Yang Principle

Unsupervised Deep Learning: A Short Review

From Neural PCA to Deep Unsupervised Learning

Part 2: Applications
Two Decades of Local Binary Patterns – A Survey

Subspace Approach in Spectral Color Science

From Pattern Recognition Methods to Machine Vision Applications

Advances in Visual Concept Detection: Ten Years of TRECVID

On the Applicability of Latent Variable Modeling to Research System Data

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