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Variable kernel density estimation in high-dimensional feature spaces

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dc.contributor.author Van der Walt, Christiaan M
dc.contributor.author Barnard, E
dc.date.accessioned 2017-09-18T06:47:52Z
dc.date.available 2017-09-18T06:47:52Z
dc.date.issued 2017-02
dc.identifier.citation Van der Walt, C.M. & Barnard, E. 2017. Variable kernel density estimation in high-dimensional feature spaces. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 4-9 February 2017, San Francisco, California, USA en_US
dc.identifier.uri https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14737
dc.identifier.uri http://hdl.handle.net/10204/9562
dc.description Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 4-9 February 2017, San Francisco, California, USA en_US
dc.description.abstract Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum-likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered. en_US
dc.language.iso en en_US
dc.publisher Association for the Advancement of Artificial en_US
dc.relation.ispartofseries Worklist;19424
dc.subject Machine learning en_US
dc.subject Probability density estimation en_US
dc.subject Non-parametric density estimation en_US
dc.subject Kernel bandwidth estimation en_US
dc.subject Maximum-likelihood en_US
dc.title Variable kernel density estimation in high-dimensional feature spaces en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Van der Walt, C. M., & Barnard, E. (2017). Variable kernel density estimation in high-dimensional feature spaces. Association for the Advancement of Artificial. http://hdl.handle.net/10204/9562 en_ZA
dc.identifier.chicagocitation Van der Walt, Christiaan M, and E Barnard. "Variable kernel density estimation in high-dimensional feature spaces." (2017): http://hdl.handle.net/10204/9562 en_ZA
dc.identifier.vancouvercitation Van der Walt CM, Barnard E, Variable kernel density estimation in high-dimensional feature spaces; Association for the Advancement of Artificial; 2017. http://hdl.handle.net/10204/9562 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Van der Walt, Christiaan M AU - Barnard, E AB - Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum-likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered. DA - 2017-02 DB - ResearchSpace DP - CSIR KW - Machine learning KW - Probability density estimation KW - Non-parametric density estimation KW - Kernel bandwidth estimation KW - Maximum-likelihood LK - https://researchspace.csir.co.za PY - 2017 T1 - Variable kernel density estimation in high-dimensional feature spaces TI - Variable kernel density estimation in high-dimensional feature spaces UR - http://hdl.handle.net/10204/9562 ER - en_ZA


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