论文标题
一步计算的KNN分类
KNN Classification with One-step Computation
论文作者
论文摘要
KNN分类是一种即兴学习模式，仅当预测测试数据设置合适的K值并从整个训练样本空间中搜索最近的K邻居时，才能进行它们，将其转介到KNN分类的懒惰部分。由于完全搜索了K最近的邻居，因此这个懒惰的部分是应用KNN分类的瓶颈问题。在本文中，提出了一步计算来代替KNN分类的懒惰部分。一步计算实际上将懒惰部分转换为矩阵计算，如下所示。在给定测试数据的情况下，首先应用训练样本以拟合最小二乘损耗函数的测试数据。然后，通过根据所有训练样本对测试数据的影响加权来生成关系矩阵。最后，lasso被用来对关系矩阵进行稀疏学习。这样，设置K值和搜索K最近的邻居都集成到统一计算。此外，提出了一项新的分类规则，以改善一步KNN分类的性能。提出的方法经过实验评估，并证明一步KNN分类是有效且有前途的
KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising
