Independent component analysis via nonparametric maximum likelihood estimation

Independent Component Analysis via Nonparametric Maximum Likelihood Estimation By

Independent Component Analysis (ICA) models are very popular semiparametric models in which we observe independent copies of a random vector X =AS, where A is a non-singular matrix and S has independent components. We propose a new way of estimating the unmixing matrix W = A−1 and the marginal distributions of the components of S using nonparametric maximum likelihood. Specifically, we study th...

Automatic Generalized Nonparametric Regression via Maximum Likelihood

A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation ...

Nonparametric Independent Component Analysis

Maximum Likelihood and Covariant Algorithms for Independent Component Analysis

Bell and Sejnowski (1995) have derived a blind signal processing algorithm for a non-linear feedforward network from an information maximization viewpoint. This paper first shows that the same algorithm can be viewed as a maximum likelihood algorithm for the optimization of a linear generative model. Second, a covariant version of the algorithm is derived. This algorithm is simpler and somewhat...

Maximum Likelihood Principal Component Analysis

PETER D. WENTZELL, DARREN T. ANDREWS, DAVID C. HAMILTON, KLAAS FABER AND BRUCE R. KOWALSKI 1 Trace Analysis Research Centre, Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4J3, Canada 2 Department of Mathematics, Statistics and Computing Science, Dalhousie University, Halifax, Nova Scotia B3H 3J5, Canada 3 Center for Process Analytical Chemistry, University of Washingto...