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
相关论文
共 50 条
  • [41] Pruning error minimization in least squares support vector machines
    de Kruif, BJ
    de Vries, TJA
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (03): : 696 - 702
  • [42] Partially linear models and least squares support vector machines
    Espinoza, M
    Suykens, JAK
    De Moor, B
    2004 43RD IEEE CONFERENCE ON DECISION AND CONTROL (CDC), VOLS 1-5, 2004, : 3388 - 3393
  • [43] Additive survival least-squares support vector machines
    Van Belle, V.
    Pelckmans, K.
    Suykens, J. A. K.
    Van Huffel, S.
    STATISTICS IN MEDICINE, 2010, 29 (02) : 296 - 308
  • [44] Recursive Update Algorithm for Least Squares Support Vector Machines
    Hoi-Ming Chi
    Okan K. Ersoy
    Neural Processing Letters, 2003, 17 : 165 - 173
  • [45] Fuzzy least squares support vector machines for multiclass problems
    Tsujinishi, D
    Abe, S
    NEURAL NETWORKS, 2003, 16 (5-6) : 785 - 792
  • [46] Efficient Sparse Least Squares Support Vector Machines for Regression
    Si Gangquan
    Shi Jianquan
    Guo Zhang
    Zhao Weili
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 5173 - 5178
  • [47] Least Squares Twin Support Vector Machines for Text Categorization
    Kumar, M. Arun
    Gopal, M.
    PROCEEDINGS OF THE 2015 39TH NATIONAL SYSTEMS CONFERENCE (NSC), 2015,
  • [48] Sparse approximation using least squares support vector machines
    Suykens, JAK
    Lukas, L
    Vandewalle, J
    ISCAS 2000: IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS - PROCEEDINGS, VOL II: EMERGING TECHNOLOGIES FOR THE 21ST CENTURY, 2000, : 757 - 760
  • [49] Building support vector machines in the context of regularized least squares
    Peng, Jian-Xun
    Rafferty, Karen
    Ferguson, Stuart
    NEUROCOMPUTING, 2016, 211 : 129 - 142
  • [50] Extended least squares support vector machines for ordinal regression
    Na Zhang
    Neural Computing and Applications, 2016, 27 : 1497 - 1509