Robust regression and outlier detection
Robust regression and outlier detection
Sensitivity analysis in linear regression
Sensitivity analysis in linear regression
The nature of statistical learning theory
The nature of statistical learning theory
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Computer Vision through Kernel Density Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Selection of Meta-parameters for Support Vector Regression
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Variable Bandwidth QMDPE and Its Application in Robust Optical Flow Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
Neural Computation
Optimally regularised kernel Fisher discriminant classification
Neural Networks
Multiple model regression estimation
IEEE Transactions on Neural Networks
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Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a bounded influence SVR, which downweights the influence of outliers in all the regression variables. The proposed approach adopts an adaptive weighting strategy, which is based on both a robust adaptive scale estimator for large regression residuals and the statistic of a "kernelized" hat matrix for leverage point removal. Thus, our algorithm has the ability to accurately extract the dominant subset in corrupted data sets. Simulated linear and nonlinear data sets show the robustness of our algorithm against outliers. Last, chemical and astronomical data sets that exhibit severe outlier contamination are used to demonstrate the performance of the proposed approach in real situations.