基于局部类相似的特征选择方法
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摘要
常用的特征选择方法利用样本空间的整个区域提取最优的特征子集.与此相反,提出一种新的局部特征选择方法,即样本空间的每个区域都与各自不同的最优特征集相关联,这些特征集能够最优地适应样本空间的局部变化.同时,在求解最优特征集对应的子空间时,基于最近邻思想,提出了一种度量测试数据与各个类相似性的方法,用来对测试样本进行分类.提出的方法可以描述为线性规划优化问题,因此可以通过简单的凸优化来求解全局最优解.在3组真实数据集和3个主流的方法上进行的对比实验,结果证明了该算法的可行性和有效性.
The common feature selection methods utilize the entire region of the sample space to extract an optimal subset of features.In contrast,this paper proposes a new local feature selection method,in which each region of the sample space is associated with a different optimal feature set,which can optimally adapt to the local variation of the sample space.Based on the concept of the nearest neighbor research,this paper proposes a method ineroduced to measure the similarity between the test datas so as to classify the tested samples.The method ineroduced in this paper can be described as the problem in a linear programming optimization,so the global optimal solution can be realized by simple convex optimization.The experimental results are gained by comparing the three real datasets and three mainstream methods demonstrate the feasibility and effectiveness of the proposed algorithm.
The common feature selection methods utilize the entire region of the sample space to extract an optimal subset of features.In contrast,this paper proposes a new local feature selection method,in which each region of the sample space is associated with a different optimal feature set,which can optimally adapt to the local variation of the sample space.Based on the concept of the nearest neighbor research,this paper proposes a method ineroduced to measure the similarity between the test datas so as to classify the tested samples.The method ineroduced in this paper can be described as the problem in a linear programming optimization,so the global optimal solution can be realized by simple convex optimization.The experimental results are gained by comparing the three real datasets and three mainstream methods demonstrate the feasibility and effectiveness of the proposed algorithm.
引文
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[4] Wang L.Feature selection with kernel class separability[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2008,30(9):1534-46.
[5] Zeng H,Cheung Y.Feature selection and kernel learning for local learning-based clustering[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(8):1532-47.
[6] Roweis,S.T.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
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[8] Boyd S,Vandenberghe L.凸优化[M].北京:清华大学出版社,2004:497-534.
[9] Thai M T.Approximation algorithms:Lp relaxation,rounding,and randomized rounding techniques[C]//Lecture Notes.Univ.Florida,2013:1-25.
[10] 周志华.机器学习[M].北京:清华大学出版社,2016:121-146.
[2] 展慧馨.基于Hu矩的主成分分析特征目标识别技术算法设计与实现[J].吉林化工学院学报,2017,34(9):27-30.
[3] 杨明,陈玲玲.基于Bandelet和分形维的手写签名鉴别系统[J].吉林化工学院学报,2014,31(7):61-63.
[4] Wang L.Feature selection with kernel class separability[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2008,30(9):1534-46.
[5] Zeng H,Cheung Y.Feature selection and kernel learning for local learning-based clustering[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(8):1532-47.
[6] Roweis,S.T.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326.
[7] Peng H,Long F,Ding C.Feature selection based on mutual information:criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2005,27(8):1226-1238.
[8] Boyd S,Vandenberghe L.凸优化[M].北京:清华大学出版社,2004:497-534.
[9] Thai M T.Approximation algorithms:Lp relaxation,rounding,and randomized rounding techniques[C]//Lecture Notes.Univ.Florida,2013:1-25.
[10] 周志华.机器学习[M].北京:清华大学出版社,2016:121-146.