Direct density-derivative estimation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sasaki, Hiroaki | - |
dc.contributor.author | Noh, Yung-Kyun | - |
dc.contributor.author | Niu, Gang | - |
dc.contributor.author | Sugiyama, Masashi | - |
dc.date.accessioned | 2022-07-15T16:02:54Z | - |
dc.date.available | 2022-07-15T16:02:54Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2016-06 | - |
dc.identifier.issn | 0899-7667 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/154408 | - |
dc.description.abstract | Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. To cope with this problem, in this letter, we propose a novel method that directly estimates density derivatives without going through density estimation. The proposed method provides computationally efficient estimation for the derivatives of any order on multidimensional data with a hyperparameter tuning method and achieves the optimal parametric convergence rate. We further discuss an extension of the proposed method by applying regularized multitask learning and a general framework for density derivative estimation based on Bregman divergences. Applications of the proposed method to nonparametric Kullback-Leibler divergence approximation and bandwidth matrix selection in kernel density estimation are also explored. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MIT PRESS | - |
dc.title | Direct density-derivative estimation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Noh, Yung-Kyun | - |
dc.identifier.doi | 10.1162/NECO_a_00835 | - |
dc.identifier.scopusid | 2-s2.0-84973349123 | - |
dc.identifier.wosid | 000377442200005 | - |
dc.identifier.bibliographicCitation | NEURAL COMPUTATION, v.28, no.6, pp.1101 - 1140 | - |
dc.relation.isPartOf | NEURAL COMPUTATION | - |
dc.citation.title | NEURAL COMPUTATION | - |
dc.citation.volume | 28 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1101 | - |
dc.citation.endPage | 1140 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | CROSS-VALIDATION | - |
dc.subject.keywordPlus | MEAN SHIFT | - |
dc.subject.keywordPlus | KERNEL | - |
dc.subject.keywordPlus | GRADIENT | - |
dc.subject.keywordPlus | ROBUST | - |
dc.subject.keywordPlus | CHOICE | - |
dc.subject.keywordPlus | RATIO | - |
dc.identifier.url | https://direct.mit.edu/neco/article-abstract/28/6/1101/8180/Direct-Density-Derivative-Estimation?redirectedFrom=fulltext | - |
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