Efficient Methods for Dealing with Missing Data in Supervised Learning
Next: Introduction
In: G. Tesauro, D. Touretzky, and T. Leen,
eds.,
Advances in Neural Information Processing Systems 7,
San Mateo, CA, Morgan Kaufman, 1995.
Efficient Methods for Dealing with Missing Data in Supervised Learning
Volker Tresp,
Siemens AG, Central Research, Otto-Hahn-Ring 6, 81730
München, Germany
Ralph Neuneier,Siemens AG,Central
Research,Otto-Hahn-Ring 6,81730
München,Germany
Subutai Ahmad, Interval Research
Corporation, 1801-C Page Mill Rd., Palo Alto, CA 94304
Abstract:
We present efficient algorithms for dealing with the problem of missing inputs
(incomplete feature vectors) during training and recall. Our approach is
based on the approximation of the input data distribution using
Parzen windows. For recall, we obtain
closed form solutions for arbitrary feedforward networks.
For training, we show how the backpropagation step for an
incomplete pattern can be approximated by a weighted
averaged backpropagation step.
The complexity of
the solutions for training and recall
is independent of the number of missing features.
We verify our theoretical
results using one classification and one regression problem.
Subutai Ahmad
Mon Mar 27 18:14:29 PST 1995