自适应近邻的极小极大标签传播

Label Propagation Through Adaptive Nearest Neighbor Based on Minimax Paths

极小极大标签传播是一种半监督分类方法,具有时间复杂度低的优势.该方法需要利用K近邻图构建稀疏相似度矩阵,对于不同的数据,如何确定K的值是个问题;如果固定K值,可能因为其值不合适导致图不连通,从而出现标签传不到某些数据,即有的数据不能被分类的问题.本文提出了一种基于自适应近邻的极小极大标签传播的方法.该方法针对图像分类问题,计算出每个样本点的自适应近邻,解决了选取的K值不合适而造成的图不连通问题,并且提高了算法的分类正确率.

Label Propagation through Minimax Paths is a semi-supervised classification method,which has the advantage of low time complexity. The method requires the sparse similarity matrix constructed by K-NN graph. For different data,how to determine the value is a problem. Inappropriate K value can result in disconnecting and make some data without a label. This paper presents a method of Label Propagation through Adaptive Nearest Neighbor based on Minimax Paths. For image classification problem,this method can calculate the adaptive nearest neighbors of each sample point,and solve the problem of disconnecting which caused by inappropriate K value meanw hile improve the classification accuracy of the algorithm.