A Discriminative Bayesian Gaussian Process Latent Variable Model for High-Dimensional Data. (arXiv:2401.16497v1 [cs.LG])

Extracting meaningful information from high-dimensional data poses a
formidable modeling challenge, particularly when the data is obscured by noise
or represented through different modalities. In this research, we propose a
novel non-parametric modeling approach, leveraging the Gaussian Process (GP),
to characterize high-dimensional data by mapping it to a latent low-dimensional
manifold. This model, named the Latent Discriminative Generative Decoder
(LDGD), utilizes both the data (or its features) and associated labels (such as
category or stimulus) in the manifold discovery process. …

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