M-estimators for robust multidimensional scaling employing ℓ2,1 norm regularization
作者：
Highlights：
• The development of a general framework based on half quadratic minimization for the solution of the MDS problem when M-estimators are employed to mitigate the outliers influence and ℓ2, 1 norm regularization is imposed for smoothness.
• The proposal of two novel algorithms derived by the general framework.
• The detailed demonstration of the merits of the proposed algorithms against the state-of-the art MDS algorithms.
• The assessment of various M-estimators.
• The study of the impact of the ℓ2, 1 norm regularization against the Frobenius norm.
摘要
•The development of a general framework based on half quadratic minimization for the solution of the MDS problem when M-estimators are employed to mitigate the outliers influence and ℓ2, 1 norm regularization is imposed for smoothness.•The proposal of two novel algorithms derived by the general framework.•The detailed demonstration of the merits of the proposed algorithms against the state-of-the art MDS algorithms.•The assessment of various M-estimators.•The study of the impact of the ℓ2, 1 norm regularization against the Frobenius norm.
论文关键词：Multidimensional scaling,Robustness,M-estimators,Half-quadratic optimization,ℓ2,1 norm regularization
论文评审过程：Received 11 January 2017, Revised 16 July 2017, Accepted 19 August 2017, Available online 30 August 2017, Version of Record 18 September 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.08.023
