基于加权局部线性 KNN 的文本分类算法.

The paper discussed classification limitation and computational complexity of categorization algorithm based on sparse representation, and proposed a novel weighted locally linear KNN algorithm for text categorization, which used weighted function to make the representation coefficients sparse, and introduced nonnegative constraints to improve the classification performances. Moreover, this paper gave the parameters experiments to select the optimization value, and gave the theoretical proof of the algorithm. The experiments show the superiority of the algorithm based on weighted locally linear KNN, which has an average improvement on 2. 49% and 0. 85% in precision and recall, compared with the traditional mcxlel. It means the text categorization algorithm based on weighted locally linear KNN has advantages of high classification accuracy and strong convergence, which is suitable for high-dimensional data classification. [ABSTRACT FROM AUTHOR]