Abstract
Principal component analysis (PCA), a statistical processing technique, transforms the data set into a lower dimensional feature space, yet retain most of the intrinsic information content of the original data. In this paper, we apply PCA for image compression. In the PCA computation, we adopt the neural network architecture in which the synaptic weights, served as the principal components, are trained through generalized Hebbian algorithm (GHA). Moreover, we partition the training set into clusters using K-means method in order to obtain better retrieved image qualities.
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