Optimization of the learning rate in the algorithm for data visualization
In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon‘s mapping. The paper describes an unsupervised backpropagation algorithm to train a multilayer feed-forward neural netwo...
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius University Press
2005-12-01
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| Series: | Lietuvos Matematikos Rinkinys |
| Subjects: | |
| Online Access: | https://www.journals.vu.lt/LMR/article/view/29205 |
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| _version_ | 1832593205103689728 |
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| author | Viktor Medvedev Gintautas Dzemyda |
| author_facet | Viktor Medvedev Gintautas Dzemyda |
| author_sort | Viktor Medvedev |
| collection | DOAJ |
| description |
In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon‘s mapping. The paper describes an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon‘s nonlinear projection. In our research the emphasis is put on the optimization of the learning rate to save computation time without losing the mapping quality.
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| format | Article |
| id | doaj-art-88504b55160d4b88af7aa5f763b5d1cf |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2005-12-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-88504b55160d4b88af7aa5f763b5d1cf2025-01-20T18:15:45ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2005-12-0145spec.10.15388/LMR.2005.29205Optimization of the learning rate in the algorithm for data visualizationViktor Medvedev0Gintautas Dzemyda1Institute of Mathematics and InformaticsInstitute of Mathematics and Informatics In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon‘s mapping. The paper describes an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon‘s nonlinear projection. In our research the emphasis is put on the optimization of the learning rate to save computation time without losing the mapping quality. https://www.journals.vu.lt/LMR/article/view/29205SAMANN networkvisualizationlearning rateSammon’s mapping |
| spellingShingle | Viktor Medvedev Gintautas Dzemyda Optimization of the learning rate in the algorithm for data visualization Lietuvos Matematikos Rinkinys SAMANN network visualization learning rate Sammon’s mapping |
| title | Optimization of the learning rate in the algorithm for data visualization |
| title_full | Optimization of the learning rate in the algorithm for data visualization |
| title_fullStr | Optimization of the learning rate in the algorithm for data visualization |
| title_full_unstemmed | Optimization of the learning rate in the algorithm for data visualization |
| title_short | Optimization of the learning rate in the algorithm for data visualization |
| title_sort | optimization of the learning rate in the algorithm for data visualization |
| topic | SAMANN network visualization learning rate Sammon’s mapping |
| url | https://www.journals.vu.lt/LMR/article/view/29205 |
| work_keys_str_mv | AT viktormedvedev optimizationofthelearningrateinthealgorithmfordatavisualization AT gintautasdzemyda optimizationofthelearningrateinthealgorithmfordatavisualization |