Optimally integrated adaptive learning

A new self-organized learning algorithm is proposed that is well suited for the problem of image compression. The network consists of a number of modules corresponding to different classes of input data. Each module consists of an orthonormal linear transformation whose weights are calculated during an initial training period. As the network is trained, each input signal x is classified according to a competitive learning scheme based on the maximum norm of the signal's projection under the class transformation. The classification is optimal in the sense that it minimizes the square error. The class transformation weights are updated according to a Hebbian learning rule which converges to the optimal Karhunen-Loeve transformation (KLT) for each class. The performance of the resulting adaptive network is shown to be superior to that of the optimal non-adaptive linear transformation.<
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