摘要
传统基于过滤的特征选择方法仅从统计或几何角度分别对数据集的每个特征计算某种得分选择特征,而忽略不同特征之间存在的联系.为解决该问题,利用局部保持投影和稀疏表示的优点,提出新的无监督特征选择算法.该方法通过限制特征权重的非负性和稀疏性选择特征.在4个基因表达数据集和2个图像数据集上的实验表明该方法是有效的.
Traditional filter-based feature selection methods calculate some scores of each feature independently to select features in a statistical or geometric perspective only, however, they ignore the correlation of different features. To solve this problem, an unsupervised feature selection method based on locality preserving projection and sparse representation is proposed. The nonnegativity and sparsity of feature weights are limited to select features in the proposed method. The experimental results on 4 gene expression datasets and 2 image datasets show that the method is effective.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2015年第3期247-252,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.71273053)资助
关键词
局部保持投影
稀疏表示
无监督
特征选择
聚类
Locality Preserving Projection, Sparse Representation, Unsupervised, Feature Selection,Clustering
