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Ren et al. recently introduced a method for aggregating multiple decisiontrees into a strong predictor by interpreting a path taken by a sample downeach tree as a binary vector and performing linear regression on top of thesevectors stacked together. They provided experimental evidence that the methodoffers advantages over the usual approaches for combining decision trees(random forests and boosting). The method truly shines when the regressiontarget is a large vector with correlated dimensions, such as a 2D face shaperepresented with the positions of several facial landmarks. However, we arguethat their basic method is not applicable in many practical scenarios due tolarge memory requirements. This paper shows how this issue can be solvedthrough the use of quantization and architectural changes of the predictor thatmaps decision tree-derived encodings to the desired output.

Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment