Dynamic Selection of Model Parameters in Principal Components
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Dynamic Selection of Model Parameters in Principal Components
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Dynamic Selection of Model Parameters in Principal Components Analysis Neural Networks Ezequiel López-Rubio, Juan Miguel Ortiz-de-Lazcano-Lobato, María del Carmen Vargas-González and José Miguel López-Rubio1 Abstract. One of the best known techniques for multidimensional data analysis is the Principal Components Analysis (PCA). A number of local PCA neural models have been proposed to partition an input distribution into meaningful clusters. Each neuron of these models uses a certain number of basis vectors to represent the principal directions of a particular cluster. Most of these neural networks are unable to learn the number of basis vectors, which is specified a priori as a fixed parameter. This leads to poor adaptation to input data. Here we develop a method where the number of basis vectors of each neuron is learned. Then we apply this method to a well known local PCA neural model. Finally, experimental results are presented where the original and modified versions of the neural model are compared.
1 INTRODUCTION The Principal Components Analysis is a multispectral data analysis technique, which is aimed to obtain the principal directions of the data, i. e., the maximum variance directions (see [6], [8]). Hence, if we have a D-dimensional input space, the PCA computes the K principal directions, where K
