A natural approach to enhance portfolio diversification is rely on factor-risk parity, which yields the whose risk equally spread among a set of uncorrelated factors. The standard choice take variance as measure, and principal components (PCs) asset returns Although PCs are unique useful for dimension reduction, they an arbitrary choice: any rotation results in This problematic because we demon...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, the linear version of the ICA problem is approached from an information-theoretic viewpoint, using Comon's framework of minimizing mutual information of the components. Using max...

We extend the framework of ICA (independent component analysis) to the case that there is a pair of information sources. The goal of MICA is to extract statistically dependent pairs of features from the sources, where the components of feature vector extracted from each source are independent. Therefore, the cost function is constructed to maximize the degree of pairwise dependence as well as o...

Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...

Blind separation of signals through the info-max algorithm may be viewed as maximum likelihood learning in a latent variable model. In this paper we present an alternative approach to maximum likelihood learning in these models, namely Bayesian inference. It has already been shown how Bayesian inference can be applied to determine latent dimensionality in principal component analysis models (Bi...

We present a method for audio-visual classification of Swedish phonemes, to be used in computer-assisted pronunciation training. The probabilistic kernel-based method is applied to the audio signal and/or either a principal or an independent component (PCA or ICA) representation of the mouth region in video images. We investigate which representation (PCA or ICA) that may be most suitable and t...