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Hebbian feature discovery improves classifier efficiency

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3 Author(s)

Two neural network implementations of principal component analysis (PCA) are used to reduce the dimension of speech signals. The compressed signals are then used to train a feedforward classification network for vowel recognition. A comparison is made of classification performance, network size, and training time for networks trained with both compressed and uncompressed data. Results show that a significant reduction in training time, fivefold in the present case, can be achieved without a sacrifice in classifier accuracy. This reduction includes the time required to train the compression network. Thus, dimension reduction, as performed by unsupervised neural networks, is a viable tool for enhancing the efficiency of neural classifiers

Published in:

Neural Networks, 1990., 1990 IJCNN International Joint Conference on

Date of Conference:

17-21 June 1990

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