Variational Bayes Factor Analysis for i-Vector Extraction
In this document we are going to derive the equations needed to implement a Variational Bayes i-vector extractor. This can be used to extract longer i-vectors reducing the risk of overfittig or to adapt an i-vector extractor from a database to another with scarce development data. This work is based on Patrick Kenny's joint factor analysis and Christopher Bishop's variational principal components.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. …
Bayesian InferenceVariational InferenceNon-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the r…
Hybrid Vector Message Passing for Generalized Bilinear Factorization
In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expec…
Approximation Properties of Variational Bayes for Vector Autoregressions
Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In …
Bayesian InferenceregressionProbabilistic Network Metrics: Variational Bayesian Network Centrality
Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function…
Gaussian Processes