Independent Component Analysis
Author | : James V. Stone |
Publisher | : MIT Press |
Total Pages | : 224 |
Release | : 2004 |
ISBN-10 | : 0262693151 |
ISBN-13 | : 9780262693158 |
Rating | : 4/5 (51 Downloads) |
Book excerpt: A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.