Sparse density estimation on multinomial manifold

Xia Hong, Junbin Gao, Sheng Chen, Tanveer Zia

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
6 Downloads (Pure)

Abstract

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first-and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.
Original languageEnglish
Pages (from-to)2972-2977
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number11
DOIs
Publication statusPublished - Nov 2015

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