Abstract
In this paper, we propose a new simplified gravitational clustering method for multi-prototype learning based on minimum classification error (MCE) training. It simulates the process of the attraction and merging of objects due to their gravity force. The procedure is simplified by not considering velocity and multi-force attraction. The proposed hierarchical method does not depend on random initialization and the results can be used as better initial centers for K-means to achieve higher performance under the SSE (sum-squared-error) criterion. The experimental results on the recognition of handwritten Chinese characters show that the proposed approach can generate better prototypes than K-means and the results obtained by MCE training can be further improved when the proposed method is employed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liu, C.-L., Jaeger, S., Nakagawa, M.: Online Recognition of Chinese Characters: The State-of-the-Art. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2), 198–213 (2004)
Rahman, A.F.R., Fairhurst, M.C.: Multi-prototype Classification: Improved Modeling of the Variability of Handwritten Data using Statistical Clustering Algorithms. Electronics Letters 33(14), 1208–1210 (1997)
Kanungo, T., et al.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)
Kohonen, T.: The Self-Organizing Map. IEEE Proceedings 78(9), 1464–1480 (1990)
Juang, B.-H., Katagiri, S.: Discriminative Learning for Minimum Error Classification. IEEE Trans. on Signal Processing 40, 3043–3054 (1992)
Huo, Q., Ge, Y., Feng, Z.-D.: High Performance Chinese OCR Based on Gabor Features, Discriminative Feature Extraction and Model Training. In: Proc. ICASSP 2001, vol. 3, pp. 1517–1520 (2001)
Katagiri, S., Juang, B.-H., Lee, C.-H.: Pattern Recognition Using a Family of Design Algorithms Based Upon the Generalized Probabilistic Descent Method. IEEE Proceedings 86(11), 2345–2373 (1998)
Chena, C.-Y., Hwanga, S.-C., Oyanga, Y.-J.: A statistics-based approach to control the quality of subclusters in incremental gravitational clustering. Pattern Recognition 38, 2256–2269 (2005)
Wang, Q., et al.: Match between Normalization Schemes and Feature Sets for Handwritten Chinese Character Recognition. In: Proc. ICDAR 2001, pp. 551–555 (2001)
Wright, W.E.: Gravitational Clustering. Pattern Recognition 9, 1149–1160 (1997)
Berkhin, P.: Survey of Clustering Data Mining Techniques. Technical Report, Accrue. Software Inc., San Jose, CA, USA (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Long, T., Jin, LW. (2006). A New Simplified Gravitational Clustering Method for Multi-prototype Learning Based on Minimum Classification Error Training. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_18
Download citation
DOI: https://doi.org/10.1007/11821045_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37597-5
Online ISBN: 978-3-540-37598-2
eBook Packages: Computer ScienceComputer Science (R0)