Density Clustering Pruning Method Based on Reconstructed Support Vectors for Sparse LS-SVM
首发时间:2015-05-13
Abstract:In least squares support vector machine (LSSVM), nonlinear function estimation is done by solving a linear set of equations instead of solving a quadratic programming problem, and a nonsparse solution is obtained. Several sparse algorithms have been developed to obtain reduced support vectors to improve the generalization performance of LSSVM. However, all of them iteratively look for support vectors in training datasets, which may are not the most superior choice for building the function model. In this paper, we propose a method of reconstructed support vectors based on the training datasets. The support vectors reconstructed are near the hyper plane of target function and uniformly distributed, which have more contribution to target function. In addition, the method we proposed converges at a faster rate than those iterative algorithms, because one-step selecting strategy is adapted without repeated training. To show the efficacy and feasibility of our proposed algorithm, some comparing experiments are conducted, which are all favorable for our viewpoints. That is, the method we proposed needs less number of support vectors to reach the almost same generalization performance, most important, which has the better robustness and accuracy prediction for the real operating mode.
keywords: density clustering lssvm reconstruct support vectors
点击查看论文中文信息
一种基于重新构造支持向量的密度聚类剪枝稀疏算法
摘要:最小二乘支持向量机是通过解线性方程组代替二次凸优化问题实现非线性函数估计,在降低复杂度的同时,会得到一个非稀疏的解。因此很多稀疏算法用来提高最小二乘支持向量机的泛化性能。但是他们大都是采用在训练过程中使用迭代法寻找支持向量的方法,这可能不是建立模型的最优方式。本文提出了一种根据训练样本集重新构造支持向量的方法,构造的支持向量均匀的分布在目标函数的超平面上,并结合相应的超参数,得到最终满足要求的模型。另外,由于本文提出的方法是采用一步到位的选择策略,在训练过程中不需要重复训练,所以具有较快的收敛速度。为了展示该算法的有效性和可行性,本文进行了对比仿真验证,实验结果证明该算法具有良好的稀疏性和鲁棒性。
论文图表:
引用
No.4641959961616143****
同行评议
共计0人参与
勘误表
一种基于重新构造支持向量的密度聚类剪枝稀疏算法
评论
全部评论0/1000