An artificial neural network model for multi dimension reduction and data structure exploration, Proceedings of International Conference of Soft Computing and Pattern Recognition (SoCPaR 2009), December 4-7, 2009
This paper proposes an hybrid artificial neural network (ANN) with self-organizing map (SOM) and modified adaptive coordinates (AC) for multivariate dimension reduction and data structures exploration. SOM, being a prominent unsupervised learning algorithm, is often used for multivariate data visual...
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| Main Authors: | , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/9982/1/An%20artificial.pdf http://ir.unimas.my/id/eprint/9982/ http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5370228 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | This paper proposes an hybrid artificial neural network (ANN) with self-organizing map (SOM) and modified adaptive coordinates (AC) for multivariate dimension reduction and data structures exploration. SOM, being a prominent unsupervised learning algorithm, is often used for multivariate data visualization. However, SOM only preserved input space inter-neurons distances and not in the output space because of SOM rigid grid. SOM grid provides little information for visual exploration of the clustering tendency of the multivariate data. Modified AC is therefore proposed to remove SOM's map rigidity and provides better data topology preserved visualization. Empirical study of the hybrid yielded promising topology preserved visualizations for synthetic and benchmarking datasets. |
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