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Published on: May 16, 2025
Implementing projection pursuit learning
1Artificial Intelligence Lab., MIT, Cambridge, MA.
Abstract:
This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a single hidden-layer sigmoidal neural network shows why grouping hidden units in a projection pursuit learning network is useful. Learning robot arm inverse dynamics is used as an example problem.
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