| Learning k for kNN Classification | |
| Zhang, Shichao1,2; Li, Xuelong3; Zong, Ming1,2; Zhu, Xiaofeng1,2; Cheng, Debo1,2; Zhu, XF (reprint author), Guangxi Key Lab MIMS, Guilin, Guangxi, Peoples R China. | |
| 作者部门 | 光学影像学习与分析中心 |
| 2017-04-01 | |
| 发表期刊 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
 |
| ISSN | 2157-6904 |
| 卷号 | 8期号:3 |
| 产权排序 | 3 |
| 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. |
| 文章类型 | Article |
| 关键词 | Knn Method Sparse Learning Missing Data Imputation |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1145/2990508 |
| 收录类别 | SCI |
| 关键词[WOS] | NEAREST NEIGHBORS ; SPARSE ; IDENTIFICATION ; PERSPECTIVE ; IMPUTATION ; ALGORITHM |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| 项目资助者 | China Key Research Program(2016YFB1000905) ; National Natural Science Foundation of China(61263035 ; China 973 Program(2013CB329404) ; Guangxi Natural Science Foundation(2015GXNSFCB139011) ; Guangxi Higher Institutions' Program of Introducing 100 High-Level Overseas Talents ; Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing ; Guangxi "Bagui" Teams for Innovation and Research ; 61573270 ; 61672177) |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
| WOS记录号 | WOS:000400160800010 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28875 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | Zhu, XF (reprint author), Guangxi Key Lab MIMS, Guilin, Guangxi, Peoples R China. |
| 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,&Zhu, XF .(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). |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Learning k for kNN C（608KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论