Algorithms for clustering data
Algorithms for clustering data
Algorithmic graph theory
Vector quantization and signal compression
Vector quantization and signal compression
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Self-organizing maps
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Clustering based on conditional distributions in an auxiliary space
Neural Computation
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
Marginal median SOM for document organization and retrieval
Neural Networks
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Order statistics learning vector quantizer
IEEE Transactions on Image Processing
Multivariate ordering in color image filtering
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised speaker recognition based on competition between self-organizing maps
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Probabilistic Self-Organizing Graphs
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Multivariate Student-t self-organizing maps
Neural Networks
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
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Two well-known variants of the self-organizing map (SOM) that are based on multivariate order statistics are the marginal median SOM and the vector median SOM. In the past, their efficiency was demonstrated for color image quantization. We employ the well-known IRIS and VOWEL data sets and we assess the SOM variants' 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, the Rand index, the @C statistic, and the overall entropy. 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 redistribute emotional speech patterns from the Danish Emotional Speech database, which were originally classified as being neutral, to the emotional states of hot anger, happiness, sadness, and surprise.