Abstract
We present a new neural model, which extends the classical competitive learning (CL) by performing a Principal Components Analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution, while retaining the dimensionality reduction properties of the PCA. Furthermore, every neuron is able to modify its behaviour to adapt to the local dimensionality of the input distribution. Hence our model has a dimensionality estimation capability. Experimental results are presented, which show the dimensionality reduction capabilities of the model with multisensor images.
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López-Rubio, E., Muñoz-Pérez, J., Gómez-Ruiz, J.A. (2003). Principal Components Analysis Competitive Learning. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_41
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DOI: https://doi.org/10.1007/3-540-44868-3_41
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