CAJ | 학술논문

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。
K최근린( KNN)분류간단고효，엄범응용우분류문제혹작위분류문제중적비교기준。단실제응용중적수거，특별시결구복잡적고유수거，기특정가능불속우구씨공간。여하선택양본특정급계산양본점간거리시KNN중적일개난제，문중충분고필각충영향인소，기우도상구역협방차특정，이용집성적방식，제출일충다협방차리-KNN분류산법。해산법충분이용KNN분류적간단유효성급리군결구적복잡수거표시화거리계산능력，유효해결복잡고유수거적분류문제。수사체수자실험험증해산법구유교호적효과。
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.