基于三元组排序局部性的SOCFS改进算法

特征选择是一种常用的机器学习降维方法,然而传统非监督特征选择算法在保持数据样本维度的局部结构时,却忽略了排序局部性对特征选择的影响。利用数据的三元组局部结构,构建数据之间的排序关系并在特征选择过程中进行局部性保持,提出基于三元组排序局部性的同时正交基聚类特征选择(SOCFS)改进算法,选择具有局部结构保持性且判别区分度高的特征。实验结果表明,与传统非监督特征选择算法相比,SOCFS改进算法聚类效果更好、收敛速度更快。

Improved SOCFS Algorithm Based on Triplet Ordinal Locality

Features selection is commonly used in dimensionality reduction of machine learning,but existing unsupervised feature selection algorithms often ignore the influence of ordinal locality on feature selection while preserving the local structure of dimensionality of data samples.To address the problem,this paper proposes an improved Simultaneous Orthogonal Basis Clustering Feature Selection(SOCFS)algorithm based on triplet ordinal locality.The algorithm uses the local structure of triplets in data to construct ordinal relationships between data,and preserves the locality of such relationships in feature selection.On this basis,the features that can preserve local structure and have high discrimination for judgment are selected.Experimental results show that the improved algorithm outperforms traditional unsupervised feature selection algorithms in terms of clustering performance and convergence speed.