基于稀疏学习的kNN分类

kNN Classification Based on Sparse Learning

在kNN算法分类问题中,k的取值一般是固定的,另外,训练样本中可能存在的噪声能影响分类结果。针对以上存在的两个问题,本文提出一种新的基于稀疏学习的kNN分类方法。本文用训练样本重构测试样本,其中,l_1-范数导致的稀疏性用来对每个测试样本用不同数目的训练样本进行分类,这解决了kNN算法固定k值问题;l_(21)-范数产生的整行稀疏用来去除噪声样本。在UCI数据集上进行实验,本文使用的新算法比原来的kNN分类算法能取得更好的分类效果。

The value of k is usually fixed in the issue of k Nearest Neighbors （kNN） classification. In addition, there may be noise in train samples which affect the results of classification. To solve these two problems, a sparse-based k Nearest Neighbors （kNN） classification method is proposed in this paper. Specifically, the proposed method reconstructs each test sample by the training data. During the reconstruction process,ι1-norm is used to generate the sparsity and different k values are used for different test samples, which solves the issue of fixed value of k. And ι21-norm is used to generate row sparsity which can remove noisy training samples. The experimental results on UCI datasets show that the proposed method outperforms the existing kNN classification method in terms of classification performance.