We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using independent component analysis (ICA). Our model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than brain sources. We discuss the properties of each of the s...

Independent component analysis (ICA), formulated as a density estimation problem, is extended to a mixture density model. A number of ICA blocks, associated to implicit equivalent classes, are updated in turn on the basis of the estimated density they represent. The approach is equivalent to the EM algorithm and allows an easy non linear extension of all the current ICA algorithms. We also show...

Recently, Independent Component Analysis (ICA) has been proposed to construct factor models in finance. According to the basic principle, the factors extracted using ICA are expected to be independent to each other. This factor model is hence named as independent factor model, in contrast to the traditional factor models which assumes uncorrelated factors. In this paper, we analyze and compare ...

A latent variable generative model with finite noise is used to describe several different algorithms for Independent Components Analysis (ICA). In particular, the Fixed Point ICA algorithm is shown to be equivalent to the ExpectationMaximization algorithm for maximum likelihood under certain constraints, allowing the conditions for global convergence to be elucidated. The algorithms can also b...

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well...

We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling. The framework incorporates a general subspace pooling with linear ICA-like layers stacked recursively. Unlike related previous models, our generative model is fully tractable: both the likelihood and the post...

Independent Component Analysis (ICA) is a powerful tool with applications in many areas of blind signal processing; however, its key assumption, i.e. the statistical independence of the source signals, can be somewhat restricting in some particular cases. For example, when considering several images, it is tempting to look on them as independent sources (the picture subjects are different), alt...

Active Shape Model (ASM) is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis (PCA) to statistically model the variability in the training set of example shapes. Independent Component Analysis (ICA) has been proven to be more efficient to extract face features than PCA . In this paper, we combine the P...

Mapping and transformation is a twin process in a high level system abstraction, which they playing corner stone of model driven architecture (MDA) technique. But the researchers on this field gave most attention for the static systems abstraction, while we find that almost systems in the world are dynamic with high frequency behavior changing. In this paper we will focus on what is the work ha...

Discrete choice models are frequently used in statistical and econometric practice. Standard models such as logit models are based on exact knowledge of the form of the link and linear index function. Semiparametric models avoid possible mis-speciication but often introduce a computational burden. It is therefore interesting to decide between approaches. Here we propose a test of semiparametric...