Semi-Supervised Kernel Methods for Regression Estimation

Author

Pozdnoukhov, Alexei ; Bengio, Samy

Author_Institution

Inst. of IDIAP Res., Ecole Polytech. Fed. de Lausanne

Volume

5

fYear

2006

fDate

14-19 May 2006

Abstract

The paper presents a semi-supervised kernel method for regression estimation in the presence of unlabeled patterns. The method exploits a recently proposed data-dependent kernel which is constructed in order to represent the inner geometry of the data. This kernel is implemented into kernel regression methods (SVR, KRR). Experimental results aim to highlight the properties of the method and its advantages as compared to fully supervised approaches. The influence of the parameters on the model properties was evaluated experimentally. One artificial and two real-world datasets were used to demonstrate the performance of the proposed algorithm

Keywords

geometry; learning (artificial intelligence); regression analysis; data-dependent kernel; geometry; regression estimation; semi-supervised kernel methods; unlabeled patterns; Geometry; Kernel; Machine learning; Machine learning algorithms; Multidimensional signal processing; Semisupervised learning; Signal processing algorithms; Support vector machine classification; Support vector machines; Unsupervised learning;

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.2006.1661341

Filename

1661341