Here, a separation theorem about Independent Subspace Analysis (ISA), a generalization of Independent Component Analysis (ICA) is proven. According to the theorem, ISA estimation can be executed in two steps under certain conditions. In the first step, 1-dimensional ICA estimation is executed. In the second step, optimal permutation of the ICA elements is searched for. We shall show that ellipt...

This paper addresses the blind source separation problem for the case where more sensors than source signals are available. A noisy-sensor model is assumed. The proposed algorithm comprises two stages, where the first stage consists of a principal component analysis (PCA) and the second one of an independent component analysis (ICA). The purpose of the PCA stage is to increase the input SNR of ...

Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly ...

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA...

It is well known that principal component analysis (PCA) only considers the second-order statistics and that independent component analysis (ICA) exploits higher-order statistics of the data. In this paper, for whitened data, we give an elegant way to incorporate higherorder statistics implicitly in the form of second-order moments, and show that ICA can be performed by PCA following a simple t...

In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performanc...

Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering...

Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this pape...

  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...

The classification of unmanned aerial vehicle hyperspectral images is great significance in agricultural monitoring. This paper studied a fine method for crops based on feature transform combined with random forest (RF). Aiming at the problem large number spectra and amount calculation, three methods dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), p...