Feb. 15, 2024, 5:43 a.m. | James Odgers, Chrysoula Kappatou, Ruth Misener, Sarah Filippi

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09122v1 Announce Type: cross
Abstract: This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) to incorporate the case where each data point comprises the weighted sum of a known number of pure component signals, observed across several input locations. Our framework allows the use of a range of priors for the weights of each observation. This flexibility enables …

abstract arxiv bayesian case cs.lg data key mixed non-parametric parametric process signal stat.ml type variables work

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