Our contribution briefly outlines the basics of the well-established technique in data mining, namely the principal component analysis (PCA), and a rapidly emerging novel method, that is, the independent component analysis (ICA). The performance of PCA singular value decomposition-based and stationary linear ICA in blind separation of artificially generated data out of linear mixtures was criti...

Title of Dissertation LEARNING ALGORITHMS FOR AUDIO AND VIDEO PROCESSING INDEPENDENT COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINE BASED APPROACHES Yuan Qi Master of Science Dissertation directed by Professor Rama Chellappa Department of Electrical and Computer Engineering In this thesis we propose two new machine learning schemes a Subband based Independent Component Analysis scheme and a hybr...

Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, wh...

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...

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...