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Extension Of The Generalized Hebbian Algorithm For Principal Component Extraction, Fredric M. Ham, Inho Kim
Extension Of The Generalized Hebbian Algorithm For Principal Component Extraction, Fredric M. Ham, Inho Kim
Electrical Engineering and Computer Science Faculty Publications
Principal component analysis (PCA) plays an important role in various areas. In many applications it is necessary to adaptively compute the principal components of the input data. Over the past several years, there have been numerous neural network approaches to adaptively extract principal components for PCA. One of he most popular learning rules for training a single-layer linear network for principal component extraction is Sanger's generalized Hebbian algorithm (GHA). We have extended the GHA (EGHA) by including a positive-definite symmetric weighting matrix in the representation error-cost function that is used to derive the learning rule to train the network. The …