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Generative topographic mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by maximum likelihood (ML), using the expectation-maximization (EM) algorithm. In this formulation, GTM is prone to data overfitting unless...
Generative topographic mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal with noni. i.d. data such as multivariate time series in a variant called GTM through time (GTM-TT), defined as a constrained hidden Markov model (HMM). In this paper, we provide...
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