Rm-NML is introduced as a geometrically invariant extension of NML to Riemannian manifolds, with explicit computation shown for normal distributions on hyperbolic spaces.
Modeling by shortest data description
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes an MDL-based criterion for selecting the latent dimension of linear dynamical systems that accounts for latent structure omitted in prior work.
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Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces
Rm-NML is introduced as a geometrically invariant extension of NML to Riemannian manifolds, with explicit computation shown for normal distributions on hyperbolic spaces.
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Inferring Latent dimension of Linear Dynamical System with Minimum Description Length
Proposes an MDL-based criterion for selecting the latent dimension of linear dynamical systems that accounts for latent structure omitted in prior work.