Topic modeling has been proved to be an effective method for exploratory text mining. It is a
common assumption of most topic models that a document is generated from a mixture of …

We present the first framework for comparing subspace clusterings. We propose several
distance measures for subspace clusterings, including generalizations of well-known …

We present a nonparametric hierarchical Bayesian model of document collections that
decouples sparsity and smoothness in the component distributions (ie, the``topics). In the …

Matrix decomposition methods represent a data matrix as a product of two smaller matrices:
one containing basis vectors that represent meaningful concepts in the data, and another …

Feature embedding is an emerging research area which intends to transform features from
the original space into a new space to support effective learning. Many feature embedding …

We present a probabilistic multiple cause model for the analysis of binary (0–1) data. A
distinctive feature of the aspect Bernoulli (AB) model is its ability to automatically detect and …

This paper introduces a probabilistic self-organizing map for clustering, analysis and
visualization of multivariate binary data. We propose a probabilistic formalism dedicated to …

Music genre classification has been investigated using many different methods, but most of
them build on probabilistic models of feature vectors xr which only represent the short time …

A novel method called binNMF is introduced which aimed to extract hidden information from
multivariate binary data sets. The method treats the problem in the spirit of blind source …

Component models such as factor analysis can be used to analyse spatial distributions of a
large number of different features–for instance the isogloss data in a dialect atlas, or the …