| Paper: | MLSP-P2.10 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Neural network learning | ||
| Title: | SELF ORGANIZING MAPS FOR REDUCING THE NUMBER OF CLUSTERS BY ONE ON SIMPLEX SUBSPACES | ||
| Authors: | Constantine Kotropoulos, Vassiliki Moschou, Aristotle University of Thessaloniki, Greece | ||
| Abstract: | This paper deals with N-dimensional patterns that are represented as points on the (N-1)-dimensional simplex. The elements of such patterns could be the posterior class probabilities for N classes, given a feature vector derived by the Bayes classifier for example. Such patterns form N clusters on the (N-1)-dimensional simplex. We are interested in reducing the number of clusters to N-1 in order to redistribute the features assigned to a particular class in the N-1 simplex over the remaining N-1 classes in an optimal manner by using a self-organizing map. An application of the proposed solution to the re-assignment of emotional speech features classified as neutral into the emotional states of anger, happiness, surprise, and sadness on the Danish Emotional Speech database is presented. | ||
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