Abstract
Incremental hierarchical discriminant regression faces several challenging issues: (a) a large input space with a small output space and (b) nonstationary statistics of data sequences. In the first case (a), there maybe few distinct labels in the output space while the input data distribute in a high dimensional space. In the second case (b), a tree has to be grown when only a limited data sequence has been observed. In this paper, we present the Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) algorithm. A novel node self-organization and spawning strategy is proposed to generate a more discriminant subspace by forming multiple clusters for one class. The algorithm was successfully applied to different kinds of data set.
The work is supported in part by National Science Foundation under grant No. IIS 9815191, DARPA ETO under contract No. DAAN02-98-C-4025, and DARPA ITO under grant No. DABT63-99-1-0014.
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
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, NewYork (1993)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, NewYork (2001)
Hwang, W., Weng, J.: Hierarchical discriminant regression. IEEE Trans. Pattern Analysis and Machine Intelligence 22(11), 1277–1293 (2000)
Hwang, W., Weng, J.: Incremental hierarchical discriminant regression for indoor visual navigation. In: Int’l Conf. on Image Processing, Thessaloniki, Greece (October 2001)
Murthy, S.: Automatic construction of decision trees from data: A multidisciplinary survey. Data Mining and Knowledge Discovery (1998)
Murthy, S., Kasit, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence 2, 1–33 (1994)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for facerecognition algorithms. pami 22, 1090–1103 (2000)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Roger, R., Benedetto, D., Schmidt, J.: Using the analytic hiearchy process in new product screening. The jouranl of Product Innovation Management 16(1), 65–76 (1999)
Swets, D.L., Weng, J.: Hierarchical discriminant analysis for image retreival. IEEE Trans. Pattern Analysis and Machine Intelligence (1997) (under 2nd-round review)
Weng, J., Hwang, W.: An incremental learning algorithm with automatically derived discriminating features. In: Asian Conference on Computer Vision, Taipei, Taiwan, pp. 426–431 (January 2000)
Weng, J., McClelland, J., Pentland, A., Sporns, O., StockMan, I., Sur, M., Thelen, E.: Autonomous mental development by robots and animals. Science 291, 599–600 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, X., Weng, J., Calantone, R. (2003). Locally Balanced Incremental Hierarchical Discriminant Regression. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-45080-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
eBook Packages: Springer Book Archive