Growing Regression Forests by Classification: Applications to Object Pose Estimation

In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5% and 22.5% error reduction respectively).Comment: Paper accepted by ECCV 201

UNSUPERVISED RANDOM FORESTS AS APPLIED TO THE ANALYSIS OF SINGLE CELL RNA SEQUENCING DATA

Single-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes