TY - JOUR
T1 - Regularized modal regression with applications in cognitive impairment prediction
AU - Wang, Xiaoqian
AU - Chen, Hong
AU - Cai, Weidong
AU - Shen, Dinggang
AU - Huang, Heng
N1 - Funding Information:
This work was partially supported by U.S. NSF-IIS 1302675, NSF-IIS 1344152, NSF-DBI 1356628, NSF-IIS 1619308, NSF-IIS 1633753, NIH AG049371. Hong Chen was partially supported by National Natural Science Foundation of China (NSFC) 11671161. We are grateful to the anonymous NIPS reviewers for the insightful comments.
Publisher Copyright:
© 2017 Neural information processing systems foundation. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort data.
AB - Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise. On the theoretical side, we establish the approximation estimate for learning the conditional mode function, the sparsity analysis for variable selection, and the robustness characterization. On the application side, we applied our model to successfully improve the cognitive impairment prediction using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort data.
UR - http://www.scopus.com/inward/record.url?scp=85047018687&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85047018687
SN - 1049-5258
VL - 2017-December
SP - 1449
EP - 1459
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
T2 - 31st Annual Conference on Neural Information Processing Systems, NIPS 2017
Y2 - 4 December 2017 through 9 December 2017
ER -