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Dimensionality Reduction as Probabilistic Inference
Paper Details
Published: 2023/11/10
DOI: 10.48550/arXiv.2304.07658
ARXIV ID: 2304.07658.
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data. DR is a critical step in many analysis pipelines as it enables visualisation, noise reduction and efficient downstream processing of the data. In this paper, the team introduce the ProbDR variational framework, which interprets a wide range of classical DR algorithms as probabilistic inference algorithms.
