Author : Hamid Mousavi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)
Book Synopsis Non-linear Latent Variable Models for Inference and Learning from Non-Gaussian Data by : Hamid Mousavi
Download or read book Non-linear Latent Variable Models for Inference and Learning from Non-Gaussian Data written by Hamid Mousavi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a family of probabilistic generative models that encompasses a variety of probability distributions (including Gaussian, Gamma, Beta, Poisson and many more) from the exponential family. In addition, we investigate a point-wise maximum function and introduce a novel non-linear superposition for coupling the latents and observables using two matrices (if the considered noise distribution has two parameters): One to model the component means and another for component variances. We further exploit the Expectation Maximization (EM) algorithm and show that the presented link function allows for the derivation of a very general and concise set of parameter update equations. Concretely, we derive a set of updates that have the same functional form for all regular distributions of the exponential family. Our results then provide directly applicable learning equations for commonly as well as for unusually distributed data. Finally, to assess the reliability of our theoretical findings, we consider different applications of the proposed generative models and investigate a variety of complex datasets including both synthetic and real data.