Competitive learning algorithms for vector quantization
Neural Networks
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding nonlinear dynamics
Understanding nonlinear dynamics
Dimension reduction by local principal component analysis
Neural Computation
A unifying review of linear Gaussian models
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal transforms for multispectral and multilayer image coding
IEEE Transactions on Image Processing
Information Maximization in a Linear Manifold Topographic Map
Neural Processing Letters
A genetic approach to data dimensionality reduction using a special initial population
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Intrinsic dimensionality maps with the PCASOM
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Regularized soft K-means for discriminant analysis
Neurocomputing
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We present a new neural model that extends the classical competitive learning 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 behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.