Locally Balanced Incremental Hierarchical Discriminant Regression
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.
KeywordsLinear Discriminant Analysis Face Image Regression Tree Input Space Output Space
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