Publication

A comparison of mixture models for density estimation

1999
Conference Papers

Résumé

Gaussian mixture models (GMMs) are a popular tool for density estimation. However, these models are limited by the fact that they either impose strong constraints on the covariance matrices of the component densities or no constraints at all. This paper presents an experimental comparison of GMMs and the recently introduced mixtures of linear latent variable models. It is shown that the latter models are a more flexible alternative for GMMs and often lead to improved results.

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1999
Conference Papers

Résumé

Source officielle

Concepts associés (32)

Modèle de mélange

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population.

Covariance matrix

In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself). Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions.

Density estimation

In statistics, probability density estimation or simply density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population. A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization.

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Publications associées (7)

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A comparison of mixture models for density estimation

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