Nonparametric Divergence Estimation and its Applications to Machine Learning
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Summary
This work proposes new nonparametric, consistent estimators for a large family of divergences and describes how to apply them for machine learning problems, and presents empirical results on synthetic data, real word images, and astronomical data sets.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 4
- References
- 67
- Access
- Open access
- OpenAlex
- https://openalex.org/W40587903
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:117251403
Keywords
Bhattacharyya distance, Estimator, Nonparametric statistics, Artificial intelligence, Hellinger distance
References
- Rényi Divergence and Its Properties
- General study of group membership. II. Determination of nearby groups.
- ESTIMATION OF ENTROPIES AND DIVERGENCES VIA NEAREST NEIGHBORS
- A Test for Normality Based on Sample Entropy
- Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions
- Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV
- Probabilistic Latent Semantic Analysis
- Empirical estimation of entropy functionals with confidence
- Independent subspace analysis using geodesic spanning trees
- Parametric Bayesian Estimation of Differential Entropy and Relative Entropy
- A class of Rényi information estimators for multidimensional densities
- Fractal random walks from a variational formalism for Tsallis entropies.
- A global geometric framework for nonlinear dimensionality reduction.
- Correction: A class of Rényi information estimators for multidimensional densities
- Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
- A new class of random vector entropy estimators and its applications in testing statistical hypotheses
- Causality detection based on information-theoretic approaches in time series analysis
- A nonparametric estimate of a multivariate density function
- Nonlinear dimensionality reduction by locally linear embedding.
- Applications of entropic spanning graphs
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