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Exploring Predictive States via Cantor Embeddings and Wasserstein Distance
[article]
2022
arXiv
pre-print
Predictive states for stochastic processes are a nonparametric and interpretable construct with relevance across a multitude of modeling paradigms. Recent progress on the self-supervised reconstruction of predictive states from time-series data focused on the use of reproducing kernel Hilbert spaces. Here, we examine how Wasserstein distances may be used to detect predictive equivalences in symbolic data. We compute Wasserstein distances between distributions over sequences ("predictions"),
arXiv:2206.04198v1
fatcat:sexbqqc3gfgp3ar7wcesfv4am4