Dataset for Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm

Citation Author(s):
Wanli
Qiao
Department of Statistics, George Mason University
Amarda
Shehu
Department of Computer Science, George Mason University
Submitted by:
Wanli Qiao
Last updated:
Sun, 03/14/2021 - 21:38
DOI:
10.21227/331n-7019
License:
0
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Abstract 

The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model. We also demonstrate the utility of the algorithm for data-enabled discovery through an application on biomolecular structure data. An extension to subspace constrained mean shift (SCMS) algorithm used to extract ridges of regression functions is briefly discussed.

Instructions: 

Biomolecular structure data analyzed in "Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm" by Wanli Qiao and Amarda Shehu.

Comments

nice

Submitted by Param Shah on Fri, 03/19/2021 - 03:25