THEORETICAL CONCEPTS & APPLICATIONS OF INDEPENDENT COMPONENT ANALYSIS

Nonlinear Independent Component Analysis: Theoretical Review and Applications

This paper reviews the Nonlinear Independent Components Analysis and its applications to blind source separation. An overview of the main statistical principles that guide the search for the independent components is formulated. The uniqueness of solution and some algorithms for estimating the nonlinear independent components are discussed. Experimental results using a synthetic database are us...

Applications of Independent Component Analysis

​Rank based Least-squares Independent Component Analysis

  In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

Independent component analysis: algorithms and applications

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 origi...

Feature Extraction of Concepts by Independent Component Analysis

Semantic clustering is important to various fields in the modern information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a...