Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides a...

This article describes a relatively new research topic called independent component analysis (ICA), which is becoming very popular in the signal processing literature and amongst those working in machine learning and data mining. The primary focus of ICA is to resolve the classical problem of blind source separation (BSS), in which an unknown mixture of nonGaussian signals is decomposed into it...

In face recognition, feature extraction techniques attempts to search for appropriate representation of the data. However, when the feature dimension is larger than the samples size, it brings performance degradation. Hence, we propose a method called Normalization Discriminant Independent Component Analysis (NDICA). The input data will be regularized to obtain the most reliable features from t...

Topographic and overcomplete representations of natural images/videos are important problems in computational neuroscience. We propose a new method using both topographic and overcomplete representations of natural images, showing emergence of properties similar to those of complex cells in primary visual cortex (V1). This method can be considered as an extension of model in Hyvärinen et al. [T...

in this paper a novel process monitoring scheme for reducing the type і and type іі error rates in the monitoring phase is proposed. first, the proposed approach uses an augmented data matrix to implement the process dynamic. then, we apply independent component analysis (ica) transformation to the augmented data matrix, and eliminate the outliers using the local outlier factor (lof) algorithm....

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...

abstract brian computer interface (bci) is a direct communication pathway between the brain and an external device. bcis are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. in this work a new algorithm is introduced to enhancing eeg signals that have been concerned the p300 problem. signal to noise ratio of eeg signals is very low and have  much art...

Independent component analysis is often approached from an information theoretic perspective employing specific sample estimates for the mutual information between the separated outputs. These approximations involve the nonparametric estimation of signal entropies. The common approach involves the estimation of these quantities and adaptation based on these criteria. In contrast, in this paper,...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, the linear version of the ICA problem is approached from an information-theoretic viewpoint, using Comon's framework of minimizing mutual information of the components. Using max...

We extend the framework of ICA (independent component analysis) to the case that there is a pair of information sources. The goal of MICA is to extract statistically dependent pairs of features from the sources, where the components of feature vector extracted from each source are independent. Therefore, the cost function is constructed to maximize the degree of pairwise dependence as well as o...