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A Novel Sparse Least Squares Support Vector Machines

机译：一种新颖的稀疏最小二乘支持向量机

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摘要

The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm-the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix. These attributes together contribute to its extreme sparseness. Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.
机译：最小二乘支持向量机（LS-SVM）的解决方案存在稀疏性问题。前向最小二乘近似（FLSA）是具有最小二乘损失函数的贪婪近似算法。本文提出了一种新的支持向量机，其中FLSA是训练算法-前向最小二乘近似SVM（FLSA-SVM）。这种新的FLSA-SVM的主要新颖之处在于，支持向量的数量是用于调整泛化能力和训练成本之间权衡的正则化参数。 FLSA-SVM还可以检测输入Gramian矩阵的向量中的线性相关性。这些属性共同导致其极端稀疏。基准数据集上的实验表明，与各种SVM算法相比，FLSA-SVM非常紧凑，同时保持了竞争性的泛化能力。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第1期|602341.1-602341.10|共10页
  • 作者

    Xiao-Lei Xia; Weidong Jiao; Kang Li; George Irwin;

  • 作者单位

    School of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China;

    School of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China,School of Engineering, Zhejiang Normal University, Jinhua 321004, China;

    School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast BT9 5AH, UK;

    School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast BT9 5AH, UK;

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