In this paper, we address the classification and dimensionality reduction via ensembles of Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a diverse representation of latent spaces represented by an ensemble of GPLVMs. Each GPLVM of the ensemble has its own projections of the high dimensional observed data on a low dimensional latent space. These models are weighted using importance sampling. Since in practical settings, neither the kernel of the GPLVM nor the dimension of the latent space is known, it is logical to engage an ensemble of GPLVMs based on different kernels and for each of them estimate the dimension of the lower dimensional space. We demonstrate the advantage of working with ensembles for classification and show the performance of dimensionality reduction of our method with numerical simulations.
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Ensembles of Gaussian process latent variable models
OpenAlex · Gaussian Processes and Bayesian Inference · 2022
Abstract
In this paper, we address the classification and dimensionality reduction via ensembles of Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a diverse representation of latent spaces represented by an ensemble of GPLVMs. Each GPLVM of the ensemble has its own projections of the high dimensional observed data on a low dimensional latent space. These models are weighted using importance sampling. Since in practical settings, neither the kernel of the GPLVM nor the dimension of the latent space is known, it is logical to engage an ensemble of GPLVMs based on different kernels and for each of them estimate the dimension of the lower dimensional space. We demonstrate the advantage of working with ensembles for classification and show the performance of dimensionality reduction of our method with numerical simulations.