Mixtures of Factor Analyzers with Common Factor Loadings for the Clustering and Visualisation of High-Dimensional Data
نویسندگان
چکیده
Mixtures of factor analyzers enable model-based density estimation and clustering to be undertaken for high-dimensional data, where the number of observations n is very large relative to their dimension p. In practice, there is often the need to reduce further the number of parameters in the specification of the componentcovariance matrices. To this end, we propose the use of common component-factor loadings, which considerably reduces further the number of parameters. Moreover, it allows the data to be displayed in low-dimensional plots.
منابع مشابه
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data
MOTIVATION Mixtures of factor analyzers enable model-based clustering to be undertaken for high-dimensional microarray data, where the number of observations n is small relative to the number of genes p. Moreover, when the number of clusters is not small, for example, where there are several different types of cancer, there may be the need to reduce further the number of parameters in the speci...
متن کاملModelling high-dimensional data by mixtures of factor analyzers
We focus on mixtures of factor analyzers from the perspective of a method for model-based density estimation from high-dimensional data, and hence for the clustering of such data. This approach enables a normal mixture model to be 5tted to a sample of n data points of dimension p, where p is large relative to n. The number of free parameters is controlled through the dimension of the latent fac...
متن کاملA mixture of generalized hyperbolic factor analyzers
The mixture of factor analyzers model, which has been used successfully for the model-based clustering of high-dimensional data, is extended to generalized hyperbolic mixtures. The development of a mixture of generalized hyperbolic factor analyzers is outlined, drawing upon the relationship with the generalized inverse Gaussian distribution. An alternating expectation-conditional maximization a...
متن کاملExtending mixtures of multivariate t-factor analyzers
Model-based clustering typically involves the development of a family of mixture models and the imposition of these models upon data. The best member of the family is then chosen using some criterion and the associated parameter estimates lead to predicted group memberships, or clusterings. This paper describes the extension of the mixtures of multivariate t-factor analyzers model to include co...
متن کاملAdaptive Mixtures of Factor Analyzers
A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mixture of factor analyzers, carrying out simultaneous clustering and locally l...
متن کامل