Advances in Self-Organizing Maps pp 173-182 | Cite as
Online Visualization of Prototypes and Receptive Fields Produced by LVQ Algorithms
Abstract
A new approach is proposed to visualize online the training of learning vector quantization algorithms. The prototypes and data samples associated to each receptive field are projected onto a two-dimensional map by using a non-linear transformation of the input space. The mapping finds a set of projection vectors by minimizing a cost function, which preserves the local topology of the input space. The proposed visualization is tested on two datasets: image segmentation and pipeline. The usefulness of the method is demonstrated by studying the behavior of Generalized LVQ, Supervised Neural Gas and Harmonic to Minimum LVQ algorithms on high-dimensional datasets.
Keywords
Learning Vector Quantization Supervised Neural Gas Harmonic to Minimum Data visualization Data Projection Topology preservationPreview
Unable to display preview. Download preview PDF.
References
- 1.Kohonen, T.: Self-organizing maps. Springer-Verlag New York, Inc., Secaucus (1997)MATHCrossRefGoogle Scholar
- 2.Sato, A., Yamada, K.: Generalized Learning Vector Quantization. In: Advances in Neural Information Processing Systems, vol. 8, pp. 423–429 (1996)Google Scholar
- 3.Estévez, P.A., Figueroa, C.J.: Online data visualization using the neural gas network. Neural Networks 19(67), 923–934 (2006)MATHCrossRefGoogle Scholar
- 4.Hammer, B., Strickert, M., Villmann, T.: Supervised neural gas with general similarity measure. Neural Processing Letters 21(1), 21–44 (2005)CrossRefGoogle Scholar
- 5.Qin, A.K., Suganthan, P.N.: Initialization insensitive LVQ algorithm based on cost-function adaptation. Pattern recognition 38(5), 773–776 (2005)MATHCrossRefGoogle Scholar
- 6.Zhang, B., Hsu, M., Dayal, U.: K-harmonic means-a data clustering algorithm. Hewllet-Packard Research Laboratory Technical Report HPL-1999-124 (1999)Google Scholar
- 7.König, A.: Interactive visualization and analysis of hierarchical neural projections for data mining. IEEE Transactions on Neural Networks 11(3), 615–624 (2000)CrossRefGoogle Scholar
- 8.Demartines, P., Herault, J.: Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks 8(1), 148–154 (1997)CrossRefGoogle Scholar
- 9.Sammon, J.W.: A Nonlinear Mapping Structure Analysis. IEEE Transactions on Computers 100(5), 401–409 (1969)CrossRefGoogle Scholar
- 10.Frank, A., Asuncion, A.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2010), http://archive.ics.uci.edu/ml Google Scholar