A fast algorithm for sparse probability density function construction

Author

Xia Hong ; Sheng Chen

Author_Institution

Sch. of Syst. Eng., Univ. of Reading, Reading, UK

fYear

2013

fDate

1-3 July 2013

Firstpage

1

Lastpage

6

Abstract

A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Keywords

density functional theory; least mean squares methods; recursive estimation; regression analysis; MISE criterion; associated computational cost; kernel selection; minimum integrated square error criterion; probability density function construction; recursive algorithm; sparse kernel density estimator; Complexity theory; Computational modeling; Educational institutions; Estimation; Kernel; Optimization; Support vector machines; minimum integrated square error; probability density function; sparse modelling;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing (DSP), 2013 18th International Conference on

Conference_Location

Fira

ISSN

1546-1874

Type

conf

DOI

10.1109/ICDSP.2013.6622731

Filename

6622731