This talk is an introduction to Independent Component Analysis (ICA) and Parallel Factor Analysis (PARAFAC), the way they are related and their links with Principal Component Analysis (PCA). PCA is now a standard technique for the analysis of two-way multivariate data, i.e., data available in matrix format. However, principal components are subject to rotational in-variance. By imposing statist...

With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the ...

Face recognition is emerging as an active research area with numerous commercial and law enforcement applications. This paper presents comparative analysis of two most popular subspace projection techniques for face recognition. It compares Principal Component Analysis (PCA) and Independent Component Analysis (ICA), as implemented by the InfoMax algorithm. ORL face database is used for training...

In previous works, the authors showed advantages and drawbacks of the use of PCA and ICA by separately. In this paper, a comparison of results in the application of these methodologies is presented. Both of them exploit the advantage of using a piezoelectric active system in different phases. An initial baseline model for the undamaged structure is built applying each technique to the data coll...

Selecting differentially expressed genes with respect to some phenotype of interest is a difficult task, especially in the presence of confounding factors. We propose to use a spatiotemporal independent component analysis to model those factors, and to combine information from different spatiotemporal parameter values to improve the set of selected genes. We show on real datasets that the propo...