To handle this problem, in this paper, we propose a novel Robust Capped L1-norm Twin Support Vector Machine with Privileged Information (R-CTSVM+). The proposed pair of regularization functions (up- and down-bound) can definitely help to increase the learning model's tolerance to disturbance,...
The capped L1-norm with upper bound value is used to construct the optimization problem instead of L2-norm, which weakens the influence of outliers and noise points on the construction of two hyperplanes to a certain extent and enhances the robustness of the model. In addition, a simple and ...
For the last problem, many L1-norm based methods, such as PCA-L1 [9] and TPCA-L1 [10], were proposed. These kinds of methods replace the L2-norm with the L1-norm as basic measurements, so that their robustness can be improved. Although L1-norm based methods are more robust, they ...
Capped L1-normVGGnetCIFARConvolutional neural networkFLOPsThe blistering progress of convolutional neural networks (CNNs) in numerous applications of the real-world usually obstruct by a surge in network volume and computational cost. Recently, researchers concentrate on eliminating these issues by ...
Capped L1-normVGGnetCIFARConvolutional neural networkFLOPsThe blistering progress of convolutional neural networks (CNNs) in numerous applications of the real-world usually obstruct by a surge in network volume and computational cost. Recently, researchers concentrate on eliminating these issues by ...
Huang. Robust dictionary learning with capped l 1-norm. In Proceedings of the 24th International Conference on Artificial Intelligence, pages 3590- 3596. AAAI Press, 2015.Jiang, W.; Nie, F.; and Huang, H. 2015. Robust dictio- nary learning with capped l1 norm. Twenty-Fourth Inter- ...
The solution of the proposed method can be achieved by optimizing a pair of capped L1-norm related problems using a newly-designed effective iterative algorithm. Also, we present some theoretical analysis on existence of local optimum and convergence of the algorithm. Extensive experiments on an ...
FRTELM first replaced the inequality constraints in TELM with equality constraints, and then introduced the cappedL1-norm distance metric to replace theL2-norm distance metric in TELM. FRTELM not only retains the advantages of TELM, but also overcomes the shortcomings of TELM exaggeration of ...
To handle this problem, in this paper, we propose a novel Robust Capped L1-norm Twin Support Vector Machine with Privileged Information (R-CTSVM+). The proposed pair of regularization functions (up- and down-bound) can definitely help to increase the learning model’s tolerance to disturbance...
To avoid this shortcoming, we propose a robust twin extreme learning machine based on a soft-capped L1-normal loss function (SCTELM). It uses a soft capped L1-norm loss function. This not only overcomes the shortcomings of the hard capped loss function, but also improves the robustness of ...