We propose a new Single-Input Multiple-Output (SIMO)-modelbased ICA with information-geometric learning algorithm for highfidelity blind source separation. The SIMO-ICA consists of multiple ICAs and a fidelity controller, and each ICA runs in parallel under the fidelity control of the entire separation system. The SIMOICA can separate the mixed signals, not into monaural source signals but into...

In recent years, there has been an increasing interest in developing new algorithms for digital signal processing by applying and generalising existing numerical linear algebra tools. A recent result shows that the FastICA algorithm, a popular state-of-the-art method for linear Independent Component Analysis (ICA), shares a nice interpretation as a Newton type method with the Rayleigh Quotient ...

We propose a new method for training iterative collective classifiers for labeling nodes in network data. The iterative classification algorithm (ICA) is a canonical method for incorporating relational information into classification. Yet, existing methods for training ICA models rely on the assumption that relational features reflect the true labels of the nodes. This unrealistic assumption in...

We propose an Iterative Nonlinear Gaussianization Algorithm (INGA) which seeks a nonlinear map from a set of dependent random variables to independent Gaussian random variables. A direct motivation of INGA is to extend principal component analysis (PCA), which transforms a set of correlated random variables into uncorrelated (independent up to second order) random variables, and Independent Com...

Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology, and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level functional magnetic resonance imaging (fMRI) data where the number of subjects is ver...

Fetal Electrocardiogram (FECG) is a weak signal through placing the electrodes upon the maternal belly surface to indirectly monitor, which contains all the forms of jamming signal. So, how to separate the FECG from the strong background interference has important value of clinical application. Independent Component Analysis (ICA) is a kind of developed new Blind Source Separation (BSS) technol...

With the development of computer science and information technology, the library is developing toward information and network. The library digital process converts the book into digital information. The high-quality preservation and management are achieved by computer technology as well as text classification techniques. It realizes knowledge appreciation. This paper introduces complex network ...

In this paper, we propose the use of the flexible independent component analysis (ICA) algorithm for the acoustic echo cancellation (AEC). The flexible ICA algorithm has a parametric score function which is controlled by the Gaussianity of the source signal (speech in our case). The probability density function of the speech signal is not always the super Gaussian, hence the inflexible score fu...

We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We t...