Advances in Independent Component Analysis and Learning Machines

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 algorithmUnsupervised 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.

Part 1: Methods 1. The Initial Convergence Rate of the FastICA Algorithm: The "One-Third Rule" 2. Improved variants of the FastICA algorithm 3. A unified probabilistic model for independent and principal component analysis 4. Riemannian optimization in complex-valued ICA 5. Non-Additive Optimization 6. Image denoising via local factor analysis under Bayesian Ying-Yang principle 7. Unsupervised Deep Learning: A Short Review 8. From Neural PCA to Deep Unsupervised Learning

Part 2: Applications 9. Two Decades of Local Binary Patterns – A Survey 10. Subspace approach in Spectral Color Science 11. From pattern recognition methods to machine vision applications 12. Advances in Visual Concept Detection: Ten Years of TRECVID 13. On the applicability of latent variable modeling to research system data