Projection pursuit learning

Author

Zhao, Ying ; Atkeson, Christopher G.

Author_Institution

Artificial Intelligence Lab., MIT, Cambridge, MA, USA

Volume

i

fYear

1991

fDate

8-14 Jul 1991

Firstpage

869

Abstract

A learning model based on a nonparametric statistical technique, projection pursuit regression, is studied. Projection pursuit is a nonparametric statistical technique to find interesting low-dimensional projections of high-dimensional data sets. Projection pursuit regression approximates a function of q variables by a sum of nonlinear functions of linear combinations of the q variables, which is related to current neural network models. A training algorithm for projection pursuit learning, called backfitting, is investigated. An example of the application of this model is demonstrated

Keywords

learning systems; neural nets; nonparametric statistics; backfitting; high-dimensional data sets; low-dimensional projections; neural nets; nonparametric statistical technique; projection pursuit learning; projection pursuit regression; training algorithm; Algorithm design and analysis; Artificial intelligence; Backpropagation algorithms; Feedforward neural networks; Function approximation; Laboratories; Learning; Neural networks; Pursuit algorithms; Terminology;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155293

Filename

155293