Competitive learning algorithms for vector quantization
Neural Networks
An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension reduction by local principal component analysis
Neural Computation
Optimal transforms for multispectral and multilayer image coding
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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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 rnethods, 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 climensionality 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.