基于改进协方差特征的李-KNN分类算法

Improved Covariance Feature Based Lie-KNN Classification Algorithm

K最近邻(KNN)分类简单高效,广泛应用于分类问题或作为分类问题中的比较基准.但实际应用中的数据,特别是结构复杂的高维数据,其特征可能不属于欧氏空间.如何选择样本特征及计算样本点间距离是KNN中的一个难题,文中充分考虑各种影响因素,基于图像区域协方差特征,利用集成的方式,提出一种多协方差李-KNN分类算法.该算法充分利用KNN分类的简单有效性及李群结构的复杂数据表示和距离计算能力,有效解决复杂高维数据的分类问题.手写体数字实验验证该算法具有较好的效果.

K-nearest neighbor （ KNN ） classification is simple, efficient and widely used for classification problems or as a base of comparison. However, the data, especially those with complex high-dimensional structures, do not always belong to the Euclidean space in practical application. How to select the features of samples and calculate the distances between them is a hard problem in KNN. With full consideration of various factors, a multi-covariance Lie-KNN classification method is put forward based on the image region covariance. In this method, the simplicity and the validity of KNN and the abilities of Lie group structure to represent complex data and calculate distances are fully used. It effectively solves the classification problems of complex high-dimensional data. Experimental results on handwritten numerals verify its effectiveness.