基于随机矩阵理论的高维数据特征选择方法
发布时间：2019-06-11 17:43
【摘要】：传统特征选择方法多是通过相关度量来去除冗余特征,并没有考虑到高维相关矩阵中会存在大量的噪声,严重地影响特征选择结果。为解决此问题,提出基于随机矩阵理论(RMT)的特征选择方法。首先,将相关矩阵中符合随机矩阵预测的奇异值去除,从而得到去噪后的相关矩阵和选择特征的数量;然后,对去噪后的相关矩阵进行奇异值分解,通过分解矩阵获得特征与类的相关性;最后,根据特征与类的相关性和特征之间冗余性完成特征选择。此外,还提出一种特征选择优化方法,通过依次将每一个特征设为随机变量,比较其奇异值向量与原始奇异值向量的差异来进一步优化结果。分类实验结果表明所提方法能够有效提高分类准确率,减小训练数据规模。
[Abstract]:The traditional feature selection method is to remove the redundant features through the correlation measure, and does not take into account that there is a large amount of noise in the high-dimensional correlation matrix, and the feature selection result is seriously affected. In order to solve this problem, a feature selection method based on random matrix theory (RMT) is proposed. firstly, removing the singular value of the correlation matrix according to the prediction of the random matrix, so as to obtain the number of the noise-removing correlation matrix and the selection characteristic; then, carrying out singular value decomposition on the noise-removing correlation matrix, and obtaining the correlation of the characteristic and the class through the decomposition matrix; and finally, Feature selection is done based on the redundancy between the features and the class's dependencies and features. In addition, a feature selection optimization method is propose, by which each feature is set to a random variable, and the difference between the singular value vector and the original singular value vector is compared to further optimize the result. The results of the classification experiment show that the proposed method can effectively improve the classification accuracy and reduce the training data scale.
【作者单位】： 辽宁大学信息学院;荣科科技股份有限公司智慧城市开发部;
【基金】：国家自然科学基金资助项目(61472169,61472072,61528202,61501105) 国家973计划前期研究专项(2014CB360509) 辽宁省教育厅科学研究一般项目(L2015204)~~
【分类号】：TP301.6
，
本文编号：2497362
[Abstract]:The traditional feature selection method is to remove the redundant features through the correlation measure, and does not take into account that there is a large amount of noise in the high-dimensional correlation matrix, and the feature selection result is seriously affected. In order to solve this problem, a feature selection method based on random matrix theory (RMT) is proposed. firstly, removing the singular value of the correlation matrix according to the prediction of the random matrix, so as to obtain the number of the noise-removing correlation matrix and the selection characteristic; then, carrying out singular value decomposition on the noise-removing correlation matrix, and obtaining the correlation of the characteristic and the class through the decomposition matrix; and finally, Feature selection is done based on the redundancy between the features and the class's dependencies and features. In addition, a feature selection optimization method is propose, by which each feature is set to a random variable, and the difference between the singular value vector and the original singular value vector is compared to further optimize the result. The results of the classification experiment show that the proposed method can effectively improve the classification accuracy and reduce the training data scale.
【作者单位】： 辽宁大学信息学院;荣科科技股份有限公司智慧城市开发部;
【基金】：国家自然科学基金资助项目(61472169,61472072,61528202,61501105) 国家973计划前期研究专项(2014CB360509) 辽宁省教育厅科学研究一般项目(L2015204)~~
【分类号】：TP301.6
，
本文编号：2497362
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