Work

Persistent Homology in Sparse Regression and Its Application to Brain Morphometry

Year: 2015

Type: article

Abstract: Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a fundamental and difficult problem in learning the sparse system. In this paper, by treating the the tuning parameter as an additional dimension, persistent homological structures over the parameter space is introduced and explored. The structures are then further exploited in drastically speeding up the computation using the proposed soft-thresholding technique. The topological structures are further used as multivariate features in the tensor-based morphometry (TBM) in characterizing white matter alterations in children who have experienced severe early life stress and maltreatment. These analyses reveal that stress-exposed children exhibit more diffuse anatomical organization across the whole white matter region.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2015.2416271

Authors: Moo K. Chung, Jamie L. Hanson, Jieping Ye, Richard J. Davidson, Seth D. Pollak

Institutions: University of Wisconsin–Madison, Duke University, Arizona State University

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Mental Health, National Institute on Drug Abuse, National Center for Advancing Translational Sciences