Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1449-1459 |
| Number of pages | 11 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2017-December |
| Publication status | Published - 2017 Jan 1 |
| Externally published | Yes |
| Event | 31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States Duration: 2017 Dec 4 → 2017 Dec 9 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Regularized modal regression with applications in cognitive impairment prediction. / Wang, Xiaoqian; Chen, Hong; Cai, Weidong; Shen, Dinggang; Huang, Heng.
In: Advances in Neural Information Processing Systems, Vol. 2017-December, 01.01.2017, p. 1449-1459.Research output: Contribution to journal › Conference article
}
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
PY - 2017/1/1
Y1 - 2017/1/1
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.
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M3 - Conference article
VL - 2017-December
SP - 1449
EP - 1459
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
ER -