# Improving Nonparametric Density Estimation with Tensor Decompositions

@article{Vandermeulen2020ImprovingND, title={Improving Nonparametric Density Estimation with Tensor Decompositions}, author={Robert A. Vandermeulen}, journal={ArXiv}, year={2020}, volume={abs/2010.02425} }

While nonparametric density estimators often perform well on low dimensional data, their performance can suffer when applied to higher dimensional data, owing presumably to the curse of dimensionality. One technique for avoiding this is to assume no dependence between features and that the data are sampled from a separable density. This allows one to estimate each marginal distribution independently thereby avoiding the slow rates associated with estimating the full joint density. This is a…

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Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations

- Computer Science, MathematicsNeurIPS
- 2020

This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations, and the approach is shown to outperform existing methods, especially when mixture components overlap significantly.

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