Incremental Hierarchical Discriminant Regression

Juyang Weng, Wey-Shiuan Hwang
2007 IEEE Transactions on Neural Networks  
This paper presents Incremental Hierarchical Discriminant Regression (IHDR) which incrementally builds a decision tree or regression tree for very high dimensional regression or decision spaces by an online, real-time learning system. Biologically motivated, it is an approximate computational model for automatic development of associative cortex, with both bottom-up sensory inputs and top-down motor projections. At each internal node of the IHDR tree, information in the output space is used to
more » ... utomatically derive the local subspace spanned by the most discriminating features. Embedded in the tree is a hierarchical probability distribution model used to prune very unlikely cases during the search. The number of parameters in the coarse-to-fine approximation is dynamic and data-driven, enabling the IHDR tree to automatically fit data with unknown distribution shapes (thus, it is difficult to select the number of parameters up front). The IHDR tree dynamically assigns long-term memory to avoid the loss-of-memory problem typical with a global-fitting learning algorithm for neural networks. A major challenge for an incrementally built tree is that the number of samples varies arbitrarily during the construction process. An incrementally updated probability model, called sample size dependent negative-log-likelihood (SDNLL) metric is used to deal with large-sample size cases, small-sample size cases, and unbalanced-sample size cases, measured among different internal nodes of the IHDR tree. We report experimental results for four types of data: synthetic data to visualize the behavior of the algorithms, large face image data, continuous video stream from robot navigation, and publicly available data sets that use human defined features.
doi:10.1109/tnn.2006.889942 pmid:17385628 fatcat:fjfjv3vzvbemto5xe52n342yla