A Tree-Based Multiscale Regression Method

Frontiers in Applied Mathematics and Statistics

Jiang, Qingtang¹  Cai, Haiyan¹ 

[1] The Department of Mathematics and Computer Science, University of Missouri–St. Louis, United States

关键词: regression;    non-linear;    High dimension data;    Tree methods;    multiscale (MS) modeling;    Manifold Learning;   

DOI  :  10.3389/fams.2018.00063

学科分类：数学（综合）

来源: Frontiers

【 摘 要 】

A tree-based method for regression is proposed. In a high dimensional feature space, the method has the ability to adapt to the lower intrinsic dimension of data if the data possess such a property so that reliable statistical estimates can be performed without being hindered by the “curse of dimensionality”. The method is also capable of producing a smoother estimate for a regression function than those from standard tree methods like CART in the region where the function is smooth and also being more sensitive to discontinuities of the function than smoothing splines or other kernel methods. The estimation process in this method consists of three components: a random projection procedure that generates partitions of the feature space, a wavelet-like orthogonal system defined on a tree that allows for a thresholding estimation of the regression function based on that tree and, finally, an averaging process that averages a number of estimates from independently generated random projection trees.

【 授权许可】

CC BY