Streaming Inference for Infinite Non-Stationary Clustering. (arXiv:2205.01212v1 [cs.LG])

Web: http://arxiv.org/abs/2205.01212

Learning from a continuous stream of non-stationary data in an unsupervised
manner is arguably one of the most common and most challenging settings facing
intelligent agents. Here, we attack learning under all three conditions
(unsupervised, streaming, non-stationary) in the context of clustering, also
known as mixture modeling. We introduce a novel clustering algorithm that
endows mixture models with the ability to create new clusters online, as
demanded by the data, in a probabilistic, time-varying, and principled manner.
To achieve this, …

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