Hebbian feature discovery improves classifier efficiency
Todd K. Leen, M. Rudnick, Dan W. Hammerstrom · 1990
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