基于加权局部线性KNN的文本分类算法

针对基于稀疏表示的分类算法存在分类限制和计算复杂性等问题进行了研究。首先，改进了加权局部线性KNN文本特征表示方法和分类算法，通过对表示系数加权使其更加稀疏，引入非负约束以规避表示系数出现负的噪声干扰；其次，给出了分类器设计和算法的收敛性证明；最后，通过实验对比得出模型中各参数的优势值域。实验结果表明，改进后的算法与基础模型相比，查准率和查全率平均分别提升了2.49%和0.85%，相比于其他主流分类算法在性能上也均有明显提高。通过分析，该算法在文本分类上具有准确率高、收敛性强等优势，适用于对高维数据的文本分类。

Text categorization algorithm based on weighted locally linear KNN

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 model. 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.