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. © Springer-Verlag 2003.
CITATION STYLE
Huang, X., Weng, J., & Calantone, R. (2004). Locally balanced incremental hierarchical discriminant regression. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2690, 185–194. https://doi.org/10.1007/978-3-540-45080-1_26
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