A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models
نویسندگان
چکیده
منابع مشابه
Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling
Several new estimators of the marginal likelihood for complex non-Gaussian models are developed. These estimators make use of the output of auxiliary mixture sampling for count data and for binary and multinomial data. One of these estimators is based on combiningChib’s estimatorwith data augmentation as in auxiliarymixture sampling,while the other estimators are importance sampling and bridge ...
متن کاملIndependent Component Analysis using Gaussian Mixture Models
This paper discusses a method for performing independent component analysis exploiting Gaussian mixture models (GMMs). Previously most techniques that combine these methods have used GMMs to model the source signals. This paper considers a parsimonious method for modelling the observed signals. The GMM is fitted to the observed data using a modified version of the expectation maximisation algor...
متن کاملParallel Sampling of DP Mixture Models using Sub-Cluster Splits
We present an MCMC sampler for Dirichlet process mixture models that can be parallelized to achieve significant computational gains. We combine a nonergodic, restricted Gibbs iteration with split/merge proposals in a manner that produces an ergodic Markov chain. Each cluster is augmented with two subclusters to construct likely split moves. Unlike some previous parallel samplers, the proposed s...
متن کاملGibbs sampling for fitting finite and infinite Gaussian mixture models
This document gives a high-level summary of the necessary details for implementing collapsed Gibbs sampling for fitting Gaussian mixture models (GMMs) following a Bayesian approach. The document structure is as follows. After notation and reference sections (Sections 2 and 3), the case for sampling the parameters of a finite Gaussian mixture model is described in Section 4. This is then extende...
متن کاملA Discrimative Training Algorithm for Gaussian Mixture Speaker Models
The Gaussian mixture speaker model (GMM) is usually trained with the expectation-maximization (EM) algorithm to maximize the likelihood (ML) of observation data from an individual class. The GMM trained based the ML criterion has weak discriminative power when used as a classifier. In this paper, a discriminative training procedure is proposed to fine-tune the parameters in the GMMs. The goal o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2014
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2013.856796