How projection-pursuit learning works in high dimensions

Ying Zhao; Christopher G. Atkeson

doi:10.1117/12.140143

1 July 1992 How projection-pursuit learning works in high dimensions

Ying Zhao, Christopher G. Atkeson

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140143
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

This paper addresses an important question in machine learning: What kinds of network architectures work better on what kinds of problems? A projection pursuit learning network has a very similar structure to a one hidden layer sigmoidal neural network. A general method based on a continuous version of projection pursuit regression is developed to show that projection pursuit regression works better on angular smooth functions than on Laplacian smooth functions. There exists a ridge function approximation scheme to avoid the curse of dimensionality for approximating some class of underlying functions.

Citation Download Citation

Ying Zhao and Christopher G. Atkeson "How projection-pursuit learning works in high dimensions", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140143

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