Learning k for kNN Classification

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Learning k for kNN Classification
	Zhang, Shichao 1,2; Li, Xuelong 3; Zong, Ming 1,2; Zhu, Xiaofeng 1,2; Cheng, Debo 1,2
刊名	acm transactions on intelligent systems and technology
	2017-04-01
卷号	8 期号:3
关键词	kNN method sparse learning missing data imputation
ISSN号	2157-6904
通讯作者	zhu, xf (reprint author), guangxi key lab mims, guilin, guangxi, peoples r china.
产权排序	3
英文摘要	the k nearest neighbor (knn) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. however, setting all test data with the same k value in the previous knn methods has been proven to make these methods impractical in real applications. this article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the correlation matrix knn (cm-knn for short) classification. specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. then, a graph laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. moreover, an l(1)-norm regularizer and an l(2,1)-norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. besides for classification tasks, the knn methods (including our proposed cm-knn method) are further utilized to regression and missing data imputation. we conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing knn methods in data-mining applications, such as classification, regression, and missing data imputation.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, information systems
研究领域[WOS]	computer science
关键词[WOS]	nearest neighbors ; sparse ; identification ; perspective ; imputation ; algorithm
收录类别	SCI
语种	英语
WOS记录号	WOS:000400160800010
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28875]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Guangxi Key Lab MIMS, Guilin, Guangxi, Peoples R China 2.Guangxi Normal Univ, Coll Comp Sci & Informat Technol, Guilin, Guangxi, Peoples R China 3.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shichao,Li, Xuelong,Zong, Ming,et al. Learning k for kNN Classification[J]. acm transactions on intelligent systems and technology,2017,8(3).
APA	Zhang, Shichao,Li, Xuelong,Zong, Ming,Zhu, Xiaofeng,&Cheng, Debo.(2017).Learning k for kNN Classification.acm transactions on intelligent systems and technology,8(3).
MLA	Zhang, Shichao,et al."Learning k for kNN Classification".acm transactions on intelligent systems and technology 8.3(2017).