Visualizing asymmetric proximities with SOM and MDS models
| Autor: | Manuel Martín-Merino, Alberto Muñoz |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Self-organizing map
business.industry Cognitive Neuroscience Non symmetric Pattern recognition Object (computer science) computer.software_genre Computer Science Applications Euclidean distance Data set ComputingMethodologies_PATTERNRECOGNITION Similarity (network science) Artificial Intelligence Experimental work Artificial intelligence Data mining Multidimensional scaling business computer Mathematics |
| Zdroj: | Neurocomputing. 63:171-192 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2004.04.010 |
| Popis: | Multidimensional scaling (MDS) and self organizing maps (SOM) algorithms are useful to visualize object relationships in a data set. These algorithms rely on the use of symmetric distances or similarity measures; for instance the Euclidean distance. There are a number of relevant applications, such as text mining and DNA microarray processing for which it is worth considering non symmetric similarity measures, that allow us to properly represent hierarchical relationships. In this paper we present asymmetric versions of SOM and MDS algorithms able to deal with asymmetric proximity matrices. We also compare these approaches to the corresponding symmetric versions. Experimental work on text databases and gene expression data sets show that the asymmetric proposed algorithms outperform their symmetric counterparts. |
| Databáze: | OpenAIRE |
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