The classical ICA model assumes that observations are all linear combinations of statistically independent scalar sources. This, as well as prior assumptions on the number of sources and their distributions are often seen as the weakest aspects of the ICA source model. In this paper, we present the mathematical structure necessary for extending ICA to more flexible models of real-world data.

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which t...

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 Biomedical time series signal like electroencephalogram (EEG), electrocardiogram (ECG), etc. The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. T...

This paper presents a audio watermark detection scheme using the undetermined independent analysis. The proposed detection scheme removes audio spectrum from the watermarked audio signal. And then, ICA-based detector estimates the hidden independent sources from the received watermarked signal using BSS for underdetermined mixtures based on 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...

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a f...

microarray data have an important role in identification and classification of the cancer tissues. having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. for this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microa...

In this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis(UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

Linear independent component analysis (ICA) learns simple cell receptive fields fromnatural images.Here,we show that linear complexvalued ICA learns complex cell properties from Fourier-transformed natural images, i.e. two Gabor-like filters with quadrature-phase relationship. Conventional methods for complex-valued ICA assume that the phases of the output signals have uniform distribution. We ...

Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals. We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gaussian noise. The main technical innovation of the algorithm is to use a fixed...