Least Squares Minimum Class Variance Support Vector Machines

被引:0
|
作者
Panayides, Michalis [1 ]
Artemiou, Andreas [2 ]
机构
[1] Cardiff Univ, Sch Math, Cardiff CF24 4HQ, Wales
[2] Univ Limassol, Dept Informat Technol, CY-3025 Limassol, Cyprus
关键词
classification; principal projections; Support Vector Machine;
D O I
10.3390/computers13020034
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, we propose a Support Vector Machine (SVM)-type algorithm, which is statistically faster among other common algorithms in the family of SVM algorithms. The new algorithm uses distributional information of each class and, therefore, combines the benefits of using the class variance in the optimization with the least squares approach, which gives an analytic solution to the minimization problem and, therefore, is computationally efficient. We demonstrate an important property of the algorithm which allows us to address the inversion of a singular matrix in the solution. We also demonstrate through real data experiments that we improve on the computational time without losing any of the accuracy when compared to previously proposed algorithms.
引用
收藏
页数:13
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