基于邻域保持学习的无监督特征选择算法

Unsupervised Feature Selection Algorithm Based on Neighborhood Preserving Learning

近邻法对不相关特征的敏感性很高,利用邻域重构系数可以保持原有数据结构的优点,为此,文中提出基于邻域保持学习的无监督特征选择算法.首先根据数据样本和邻域的相似性构造相似矩阵,并引入中间矩阵构造低维空间.然后利用拉普拉斯乘子法选择有效特征子集.在4个公开数据集上的实验表明,文中算法可以有效识别代表性特征.

Since the sensitivity of neighborhood method for irrelevant features is high, an unsupervised feature selection algorithm based on neighborhood preserving learning(NPL) is proposed by utilizing the reconstruction coefficient of neighborhood to maintain the original data structure. Firstly, according to the similarity of each data and its neighborhood, the similarity matrix is constructed and a low dimensional space is built by introducing a mid-matrix. Secondly, an effective feature subset is selected by the Laplace multiplier method. Finally, the proposed algorithm is compared with six state-of-the-art feature selection methods on four publicly available datasets. Experimental results show the proposed method effectively identifies the representative features.