This paper proposes Shared Component Analysis (SCA) as an alternative to Principal (PCA) for the purpose of dimensionality reduction neuroimaging data. The trend towards larger numbers recording sensors, pixels or voxels leads richer data, with finer spatial resolution, but it also inflates cost storage and computation risk overfitting. PCA can be used select a subset orthogonal components that...

Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, intuitive accessible manner, basic principles underlying PCA its applications. Next, present a systematic, though no exclusive, survey some representative works illustrating poten...

Our contribution highlights the statistical properties and biological interpretation of the basis vectors (filters) that result from applying topographic independent component analysis (ICA) to feature extraction from patches of natural images. The consistency of the feature sets obtained from various collections of natural image data sets applying topographical ICA (TICA) supports the opinion ...

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, h...

Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) have been successfully applied to face recognition, and both are based on the second order statistics of the image set. Kernel-based subspace methods try to capture the higher order statistics of the image set and thus may provide better results for recognition purposes. In this paper, we try to compare different algorith...

We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...