Many applications can benefit from soft clustering, where each datum is assigned to multiple clusters with membership weights that sum to one. In this paper we present a comparison of principal component analysis (PCA) and independent component analysis (ICA) when used for soft clustering. We provide a short mathematical background for these methods and demonstrate their application to a sponso...

In this paper we study the dependencies of features found by independent component analysis (ICA) in image data by examining how the activation of one feature changes certain statistics of the data. We look at how the PCA components are affected when we know a certain ICA feature is highly active, and also study the ICA components in this situation. This can be thought of as a simple form of tw...

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...

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatiotemporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA...

Abstract— After summarizing typical approaches for solving independent component analysis (ICA) problems, advances on the ICA studies that consider hybrid sources of both subGaussians and superGaussians and the ICA extensions that consider noise and temporal dependence among observations have been overviewed from the perspective of Bayesian Ying-Yang independence learning. Not only new insights...

Data sets acquired from functional magnetic resonance imaging (fMRI) contain both spatial and temporal structures. In order to blindly extract underlying activities, the common approach however only uses either spatial or temporal independence. More convincing results can be achieved by requiring the transformed data to be as independent as possible in both domains. First introduced by Stone, s...