NEURAL IMPLEMENTATION OF A CLASS OF PCA LEARNING ALGORITHMS

Abstract. Principal component analysis allows the identification of a linear transformation such that the axes of the resulted coordinate system correspond to the largest variability of the investigated signal. The advantages of using principal components reside from the fact that bands are uncorrelated and no information contained in one band can be predicted by the knowledge of the other bands, therefore the information contained by each band is maximum for the whole set of bits. The paper reports a series of conclusions concerning the performance and efficiency of some of the most frequently used PCA algorithms implemented on neural architectures