Self-Organizing Maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
The lack of a priori distinctions between learning algorithms
Neural Computation
Functional Principal Component Learning Using Oja's Method and Sobolev Norms
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Distance learning in discriminative vector quantization
Neural Computation
Adaptive relevance matrices in learning vector quantization
Neural Computation
Representation of functional data in neural networks
Neurocomputing
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Window-based example selection in learning vector quantization
Neural Computation
Divergence-based vector quantization
Neural Computation
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Border-sensitive learning in kernelized learning vector quantization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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The amount of available functional data like time series and hyper-spectra in remote sensing is rapidly growing and requires an efficient processing taking into account the knowledge about this special data characteristic. Usually these data are high-dimensional but with inherent correlations between neighbored vector dimensions reflecting the functional characteristics. Especially, for such high dimensional data, metric adaptation is an important tool in several learning methods for data discrimination and sparse representation. An important group of metric learning are relevance and matrix learning in vector quantization. Functional variants of relevance and matrix learning are considered in this paper. For an efficient learning of these functional relevance and matrix weights, we propose the utilization of spatial neighborhood correlations regarding the vector dimensions. We show that this efficient enhancement scheme can be seen as a new dissimilarity measure in standard generalized learning vector quantization, emphasizing the functional data aspect, such that theoretical aspects like margin analysis remain valid.