This paper reports on numerical experiments on the ‘independent component analysis’ (ICA) of some nonGaussian stochastic processes. It is found that the orthonormal basis discovered by ICA are strikingly close to wavelet basis.

The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear...

The technique of independent component analysis (ICA) is applied for texture feature detection. In ICA an optimal transformation (with respect to the statistical structure of the image samples set) is discovered via blind signal processing. Any texture is considered as a mixture of independent sources (basic functions of detected transformation). Experimental comparison is documented on the com...

This work proposes a novel algorithm for independent component analysis (ICA) based on marginal density estimation. The proposed ICA algorithm aims to search for an effective demixing matrix as well as weighted Parzen window (WPW) representations for marginal densities of independent components so as to express a factorial joint density for high dimensional observations. Following the linear mi...

Independent Component Analysis (ICA) is a fundamental method for Blind Source Separation (BSS). Classical ICA takes data matrix input formed by vector data. This paper focuses on BSS with third-order tensor data, such as 2D images. Two approaches exist this problem. The first approach reshapes each into to apply classical ICA, structural information lost. second unfolds along different modes pe...

Topographic independent component analysis (TICA) is an interesting extension of the conventional ICA, which aims at finding a linear decomposition into approximately independent components with the dependence between two components is approximated by their proximity in the topographic representation. In this paper we apply the topographic ICA to gene expression time series data and compare it ...

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result detailed inference underlying regulatory networks, but the diversity experimental platforms and protocols introduces critical biases that hinder scalable analysis existing data. Here, we show structure E . coli transcriptome, as determined by Independent Component Analysis (ICA...

Recently, the application of Independent Component Analysis (ICA) to natural images has raised a great interest. Some outstanding features have been observed, like the sparse distribution of the independent components and the special appearance of the ICA bases (most of them look like edges). This paper provides a new insight on this behaviour, being supported by experimental results. In partic...

Independent component analysis (ICA) attempts to nd a linear decomposition of observed data vectors into components that are statistically independent. It is well known, however, that such a decomposition cannot be exactly found, and in many practical applications, independence is not achieved even approximately. This raises the question on the utility and interpretation of the components given...

One of the reasons ICA (Independent Component Analysis) became so popular is that ICA is a promising tools for a lot of applications. One of the attractive applications is the biological data analysis. There are a lot of works on neurobiological data analysis such as EEG (Electroencephalography), fMRI (functional Magnetic Resonance Imaging), and MEG (Magnetoencephalography), and they show inter...