Algorithms for clustering data
Algorithms for clustering data
Algorithmic graph theory
Vector quantization and signal compression
Vector quantization and signal compression
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Order statistics learning vector quantizer
IEEE Transactions on Image Processing
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Two well-known variants of the self-organizing map (SOM) that are based on order statistics are the marginal median SOM and the vector median SOM. In the past, their efficiency was demonstrated for color image quantization. In this paper, we employ the well-known IRIS data set and we assess their performance with respect to the accuracy, the average over all neurons mean squared error between the patterns that were assigned to a neuron and the neuron’s weight vector, and the Rand index. All figures of merit favor the marginal median SOM and the vector median SOM against the standard SOM. Based on the aforementioned findings, the marginal median SOM and the vector median SOM are used to re-distribute emotional speech patterns from the Danish Emotional Speech database that were originally classified as being neutral to four emotional states such as hot anger, happiness, sadness, and surprise.