Estimation of Minimum Measure Sets in Reproducing Kernel Hilbert Spaces and Applications.

Author

Davy, Manuel ; Desobry, Frédéric ; Canu, Stéphane

Author_Institution

LAGIS/CNRS, Ecole Centrale Lille

Volume

3

fYear

2006

fDate

14-19 May 2006

Abstract

Minimum measure sets (MMSs) summarize the information of a (single-class) dataset. In many situations, they can be preferred to estimated probability density functions (pdfs): they are strongly related to pdf level sets while being much easier to estimate in large dimensions. The main contribution of this paper is a theoretical connection between MMSs and one class support vector machines. This justifies the use of one-class SVMs in the following applications: novelty detection (we give explicit convergence rate) and change detection

Keywords

Hilbert spaces; probability; signal detection; support vector machines; change detection; kernel Hilbert spaces; minimum measure sets estimation; novelty detection; one class support vector machines; probability density functions; Convergence; Extraterrestrial measurements; Hilbert space; Image processing; Kernel; Probability density function; Q measurement; Signal processing; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660742

Filename

1660742