The Block Generative Topographic Mapping
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Document analysis and visualization with zero-inflated poisson
Data Mining and Knowledge Discovery
Self-organizing mixture models
Neurocomputing
Probabilistic self-organizing maps for qualitative data
Neural Networks
Weighted topological clustering for categorical data
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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A nonlinear latent variable model for the topographic organization and subsequent visualization of multivariate binary data is presented. The generative topographic mapping (GTM) is a nonlinear factor analysis model for continuous data which assumes an isotropic Gaussian noise model and performs uniform sampling from a two-dimensional (2-D) latent space. Despite the, success of the GTM when applied to continuous data the development of a similar model for discrete binary data has been hindered due, in part, to the nonlinear link function inherent in the binomial distribution which yields a log-likelihood that is nonlinear in the model parameters. The paper presents an effective method for the parameter estimation of a binary latent variable model-a binary version of the GTM-by adopting a variational approximation to the binomial likelihood. This approximation thus provides a log-likelihood which is quadratic in the model parameters and so obviates the necessity of an iterative M-step in the expectation maximization (EM) algorithm. The power of this method is demonstrated on two significant application domains, handwritten digit recognition and the topographic organization of semantically similar text-based documents