Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data
Abstract:We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.
Keywords: multinomial dirichlet classification model, variational Bayesian approximation, approximate posterior distribution, Gaussian process priors, Importance sampling, Marginal likelihood evidence
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110389Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1330
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