Abstract. Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed t...
In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of...
The class of finite mixtures of multivariate Bernoulli distributions is known to be nonidentifiable, i.e., different values of the mixture parameters can correspond to exactly the...
Efficient probability density function estimation is of primary interest in statistics. A popular approach for achieving this is the use of finite Gaussian mixture models. Based on...
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...