CAJ | 학술논문

为解决k‐NN算法中固定k的选定问题，引入稀疏学习和重构技术用于最近邻分类，通过数据驱动（data‐driven）获得k值，不需人为设定。由于样本之间存在相关性，用训练样本重构所有测试样本，生成重构系数矩阵，用 l1‐范数稀疏重构系数矩阵，使每个测试样本用它邻域内最近的k （不定值）个训练样本来重构，解决k‐NN算法对每个待分类样本都用同一个k值进行分类造成的分类不准确问题。UCI数据集上的实验结果表明，在分类时，改良k‐NN算法比经典k‐NN算法效果要好。
위해결k‐NN산법중고정k적선정문제，인입희소학습화중구기술용우최근린분류，통과수거구동（data‐driven）획득k치，불수인위설정。유우양본지간존재상관성，용훈련양본중구소유측시양본，생성중구계수구진，용 l1‐범수희소중구계수구진，사매개측시양본용타린역내최근적k （불정치）개훈련양본래중구，해결k‐NN산법대매개대분류양본도용동일개k치진행분류조성적분류불준학문제。UCI수거집상적실험결과표명，재분류시，개량k‐NN산법비경전k‐NN산법효과요호。
To deal with the problem that k‐NN algorithm selects the fixed k ,the sparse learning and reconstruction techniques for classification were used ,so that k value was obtained through data‐driven without artificial set .Due to the existence correla‐tion between the samples ,every test sample was used to reconstruct all the training samples ,reconstruction coefficient matrix was generated .The l1‐norm was used to penalize the objective function ,so that each test sample used its neighborhood nearest k (a variable value) training samples to reconstruct ,which solved the problem of inaccurate classification caused by k‐NN algo‐rithm using the fixed k value .Results of experiments on UCI datasets show that the improved k‐NN algorithm is better than the classical k‐NN algorithm in terms of classification effect .