摘要
基于谱图的无监督特征选择方法在原始高维空间构建图,易受噪声或冗余特征干扰.为此提出一种基于自适应局部保持投影的无监督特征选择方法,利用全局线性回归函数建立特征选择模型,结合自适应局部保持投影提高模型准确度,引入l2,1约束提升特征之间可区分度,避免噪声干扰.最后通过实验验证了该方法的有效性.
The unsupervised feature selection method based on spectrogram is constructed in the original high dimensional data space, which is easily disturbed by noise or redundant features. To overcome these deficiencies, an unsupervised feature selection method based on adaptive locality preserving projection is proposed. Global linear regression function is utilized to construct feature selection model, and the adaptive local preserving projection is adopted to improve model accuracy. Then the l2.1 -norm constraint is added to improve the distinguishability of different features and avoid noise interference. A comparison with several state-of-the-art feature selection methods demostrate the effectiveness of the proposed method.
作者
严菲
王晓栋
YAN Fei;WANG Xiaodong(College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)
基金
国家自然科学基金(61502405)
福建省自然基金面上项目(2017J01511)
福建省中青年教师教育科研项目(JAT160357
JAT170417)资助
关键词
特征选择
无监督学习
线性回归
局部保持投影
l2
1范数
feature selection
unsupervised learning
linear regression
locality preserving projection
l2,1-norm