ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the eld of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the numbe...

In this paper, a data-driven temporal processing method based on Independent Component Analysis (ICA) is proposed for obtaining a more robust speech representation. Two different schemes of dominant temporal filters based on ICA are investigated. The one is the perceptuallybased filter which always focuses on the modulation frequency range between 1 and 16 Hz and the other is the most independe...

This paper addresses the problem of image categorization using local sensory information which is aggregated into global cortical-like representations of diierent image categories. Local information is adaptively extracted from an image database using Independent Component Analysis (ICA) which provides a set of localized, oriented, and band-pass lters selective to independent features of the di...

Independent component analysis is a lively field of research and is being utilized for its potential in statistically independent separation of images. ICA based algorithms has been used to extract interference and mixed images and a very rapid developed statistical method during last few years. So, in this paper an efficient result oriented algorithm for ICA-based blind source separation has b...

ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the num...

Methods for obtaining representations of face images based on independent component analysis (ICA) are presented. A global ICA representation is compared to a global representation based on principal component analysis (PCA) for recognizing faces across changes in lighting and changes in pose. For each set of face images, a set of statistically independent source images was found through an uns...

Advancement in computing and telecommunications technologies, digital images and video are playing vital role in the present information era. Detection of a person from an image of their face is termed as face recognition. It is based on the classifiers applied for the feature extraction. It has its applications on various domains. Face recognition application without human intervention disting...

Independent Component Analysis (ICA) technique separates mixed signals blindly without any information of mixing system. The present work studies and analyses the issues involved in interference rejection in direct sequence spread spectrum communication systems based on Independent Component Analysis technique. The ICA technique tries to separate the unwanted interfering signal from the desired...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...