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Designing Robust Regression Models
In this study we focus on the preference among competing models from a family of polynomial regressors. Classical statistics offers a number of...
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Robust Losses in Deep Regression
What is the noise distribution of a given regression problem is not known in advance and, given that the assumption on which noise is present is...
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Locally sparse and robust partial least squares in scalar-on-function regression
We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our...
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Robust twin support vector regression with correntropy-based metric
Machine learning methods have been widely used control and information systems. Robust learning is an important issue in machine learning field. In...
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A robust adaptive linear regression method for severe noise
Up to now, the inaccurate supervision problem caused by label noises poses a big challenge for regression modeling. Regularized noise-robust models...
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Robust and sparse multinomial regression in high dimensions
A robust and sparse estimator for multinomial regression is proposed for high dimensional data. Robustness of the estimator is achieved by trimming...
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A robust quantile regression for bounded variables based on the Kumaraswamy Rectangular distribution
Quantile regression (QR) models offer an interesting alternative compared with ordinary regression models for the response mean. Besides allowing a...
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Robust estimation in regression and classification methods for large dimensional data
Statistical data analysis and machine learning heavily rely on error measures for regression, classification, and forecasting. Bregman divergence (
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Discriminative Noise Robust Sparse Orthogonal Label Regression-Based Domain Adaptation
Domain adaptation ( DA ) aims to enable a learning model trained from a source domain to generalize well on a target domain, despite the mismatch of...
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A highly efficient ADMM-based algorithm for outlier-robust regression with Huber loss
Huber robust regression (HRR) has attracted much attention in machine learning due to its greater robustness to outliers compared to least-squares...
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A Robust Extreme Learning Machine Based on Adaptive Loss Function for Regression Modeling
The extreme learning machine (ELM) algorithm is advantageous to regression modeling owing to its simple structure, fast computation, and good...
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A general robust low–rank multinomial logistic regression for corrupted matrix data classification
Multi-classification of corrupted matrix data is a significant problem in machine learning and pattern recognition. However, most of the existing...
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Robust regression via error tolerance
Real-world datasets are often characterised by outliers; data items that do not follow the same structure as the rest of the data. These outliers...
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Robust Geodesic Regression
This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting...
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Robust kernel ensemble regression in diversified kernel space with shared parameters
Kernel regression is an effective non-parametric regression method. However, such regression methods have problems in choosing an appropriate kernel...
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CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression
In recent years, estimating beauty of faces has attracted growing interest in the fields of computer vision and machine learning. This is due to the...
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Robust transfer learning for high-dimensional quantile regression model with linear constraints
Transfer learning has emerged as a crucial technique for leveraging source domain information to enhance the performance of target tasks. However,...
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Robust Twin Support Vector Regression with Smooth Truncated Hε Loss Function
Twin support vector regression (TSVR) is an important algorithm to handle regression problems developed on the basis of support vector regression...
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Zero-Norm ELM with Non-convex Quadratic Loss Function for Sparse and Robust Regression
Extreme learning machine (ELM) is a machine learning technique with simple structure, fast learning speed, and excellent generalization ability,...
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Doubly robust estimation and robust empirical likelihood in generalized linear models with missing responses
In this paper, we study doubly robust estimation and robust empirical likelihood of regression parameter for generalized linear models with missing...
