Florian T. Pokorny
Assistant Professor, School of Computer Science and Communication, KTH Royal Institute of TechnologyTopological Constraints and Kernel-Based Density Estimation
In Advances in Neural Information Processing Systems 25, Workshop on Algebraic Topology and Machine Learning, 2012
Abstract
This extended abstract explores the question of how to estimate a probability
distribution from a finite number of samples when information about the topology
of the support region of an underlying density is known.
This workshop contribution is a continuation of our recent
work combining persistent homology and kernel-based density
estimation for the first time and in which we explored
an approach capable of incorporating topological constraints in bandwidth selection.
We report on some recent experiments with
high-dimensional motion capture data which show that our
method is applicable even in high dimensions and develop
our ideas for potential future applications of this framework.
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Download this publicationBibtex
@incollection{pokorny2012b,
title = {Topological Constraints and Kernel-Based Density Estimation},
author = {Pokorny, Florian T. and Ek, Carl Henrik and Kjellstr{\"o}m, Hedvig and Kragic, Danica},
booktitle = {Advances in Neural Information Processing Systems 25, Workshop on Algebraic Topology and Machine Learning},
year = {2012},
url = {http://www.csc.kth.se/~fpokorny/static/publications/pokorny2012b.pdf},
}