Constrained Nonparametri Estimation via Mixtures

Constrained Nonparametric Estin1ation via rvIixtures

\Ve a approach to measures constrained to lie in a convex set. \Ve measures as mixtures known extreme measures, and so the of eS1Jlnlal;lng a constrained measure becomes one of estimating an unconstrained mlXmeasure. constraints arise in many such as estimation of the mean estimation under stochastic constraints. Vve describe mixture representation techniques for these and other and to maximum ...

Nonparametri Volatility Density Estimation

Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers

Mixtures of factor analyzers are becoming more and more popular in the area of model based clustering of multivariate data. According to the likelihood approach in data modeling, it is well known that the unconstrained likelihood function may present spurious maxima and singularities. To reduce such drawbacks, in this paper we introduce a procedure for parameter estimation of mixtures of factor...

Manifold Constrained Finite Gaussian Mixtures

In many practical applications, the data is organized along a manifold of lower dimension than the dimension of the embedding space. This additional information can be used when learning the model parameters of Gaussian mixtures. Based on a mismatch measure between the Euclidian and the geodesic distance, manifold constrained responsibilities are introduced. Experiments in density estimation sh...

Underdetermined Sparse Blind Source Separation with Delays

In this paper, we address the problem of under-determined blind source separation (BSS), mainly for speech signals, in an anechoic environment. Our approach is based on exploiting the sparsity of Gabor expansions of speech signals. For parameter estimation, we adopt the clustering approach of DUET [19]. However, unlike in the case of DUET where only two mixtures are used, we use all available m...