Unsupervised learning in neural computation

Abstract

In this article, we consider unsupervised learning from the point of view of applying neural computation on signal and data analysis problems. The article is an introductory survey, concentrating on the main principles and categories of unsupervised learning. In neural computation, there are two classical categories for unsupervised learning methods and models: first, extensions of principal component analysis and factor analysis, and second, learning vector coding or clustering methods that are based on competitive learning. These are covered in this article. The more recent trend in unsupervised learning is to consider this problem in the framework of probabilistic generative models. If it is possible to build and estimate a model that explains the data in terms of some latent variables, key insights may be obtained into the true nature and structure of the data. This approach is also briefly reviewed.