Independent Component Analysis
Product Description
Independent Component Analysis
A comprehensive introduction to ICA for students and practitioners
Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more.
Independent Component Analysis is divided into four sections that cover:
* General mathematical concepts utilized in the book
* The basic ICA model and its solution
* Various extensions of the basic ICA model
* Real-world applications for ICA models
Authors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.
Technical Specifications
You might also like
Deep Learning (Adaptive Computation and Machine Learning series)
Independent Component Analysis: A Tutorial Introduction (MIT Press)
Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)
Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics)
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)