Estimating Gaussian Mixture Models from Data with Missing Features
نویسنده
چکیده
Maximum likelihood (ML) tting of Gaussian mixture models (GMMs) to feature data is most e ciently handled by the EM algorithm [1, 2, 3, 4]. The EM algorithm is directly applicable to multivariate data in which all the features are always present, and there are no missing values. Unfortunately, missing values are common: caused either by random or systematic e ects. This study presents a novel algorithm for estimating the parameters of GMMs when there are random missing values. The approach is Bayesian in the missing values and ML in the GMM parameters. The same model can be applied to heteroscedastic data, and to indirectly observable mixed Gaussian observations.
منابع مشابه
An Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...
متن کاملEstimation of missing LSF parameters using Gaussian mixture models
Speech transmission over packet networks has to cope with packet delays and packet losses. When a packet loss occurs the missing information must be estimated. In this contribution we focus on restoring the spectral parameters of a speech coder. A novel approach to estimating missing Line Spectral Frequency (LSF) parameters using Gaussian Mixture Models (GMM) is proposed. We present the estimat...
متن کاملIMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملMixture of Gaussians for distance estimation with missing data
Many data sets have missing values in practical application contexts, but the majority of commonly studied machine learning methods cannot be applied directly when there are incomplete samples. However, most such methods only depend on the relative differences between samples instead of their particular values, and thus one useful approach is to directly estimate the pairwise distances between ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1996