A new approach to cluster-weighted modeling

Author

Prokhorov, Danil V. ; Feldkamp, Lee A. ; Feldkamp, Timothy M.

Author_Institution

Res. Lab., Ford Motor Co., Dearborn, MI, USA

Volume

3

fYear

2001

fDate

2001

Firstpage

1669

Abstract

We discuss an approach to joint density estimation called cluster-weighted modeling (CWM). The base approach was originally proposed by Gershenfeld (1998). We describe two innovations to the base CWM. Among these, the first enables the CWM to work with continuous streams of data. The second addresses the commonplace problem of local minima which may be encountered during the CWM parameter adjustment process. Our approach to mitigate this problem is quite elaborate, but it represents a principled way of improving the efficacy of the parameter adjustment process. We illustrate CWM and our performance enhancements with an example

Keywords

function approximation; learning (artificial intelligence); neural nets; pattern clustering; probability; cluster-weighted modeling; continuous data streams; joint density estimation; local minima; parameter adjustment process; Collaborative work; Covariance matrix; Density functional theory; Density measurement; Gaussian processes; Interpolation; Nonhomogeneous media; Optimization methods; Technological innovation; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938412

Filename

938412