Abstract
We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most significant poses on a data set. We found the better result from the sparsified Kernel PCA method.
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