Independent component analysis: algorithms and applications

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...

Blind Source Separation via Independent Component Analysis : Algorithms and Applications

Blind Source Separation (BSS) refers to the process of recovering source signals from a given mixture of unknown source signals were in no prior information about source and mixing methodology is known. Independent Component Analysis (ICA) is widely used BSS technique which allows separation of source components from complex mixture of signals based on certain statistical assumptions. This pape...

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...

Sparse Principal Component Analysis: Algorithms and Applications

Sparse Principal Component Analysis: Algorithms and Applications