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Found 4 papers, 1 papers with code
Intrinsic Dimension for Large-Scale Geometric Learning
In the present work, we derive a computationally feasible method for determining said axiomatic ID functions.
Skew-amenability of topological groups
We prove characterizations of skew-amenability for topological groups of isometries and automorphisms, clarify the connection with extensive amenability of group actions, establish a F{\o}lner-type characterization, and discuss closure properties of the class of skew-amenable topological groups.
Group Theory Functional Analysis
Intrinsic dimension and its application to association rules
This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures.
Intrinsic Dimension of Geometric Data Sets
The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension.
