基于稀疏学习的kNN分类

doi:10.16088/j.issn.1001-6600.2016.03.006

Journal of Guangxi Normal University(Natural Science Edition) ›› 2016, Vol. 34 ›› Issue (3): 39-45.doi: 10.16088/j.issn.1001-6600.2016.03.006

Previous Articles Next Articles

kNN Classification Based on Sparse Learning

ZONG Ming^1,2, GONG Yonghong³, WEN Guoqiu¹, CHENG Debo^1,2, ZHU Yonghua⁴

1.College of Computer Science and Information Technology,Guangxi Normal University,Guilin Guangxi 541004,China;
2.Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, Guigang Guangxi 537000,China;
3.Department of Information Engineering,Guilin University of Aerospace Technology, GuilinGuangxi 541004,China;
4.School of Computer,Electronics and Information, Guangxi University,Nanning Guangxi 530004,China

Received:2015-09-09 Online:2016-09-30 Published:2018-09-17

Abstract

Abstract: The value of k is usually fixed in the issue of k Nearest Neighbors (kNN) classification. In addition, there may be noise in train samples which affect the results of classification. To solve these two problems, a sparse-based k Nearest Neighbors (kNN) classification method is proposed in this paper. Specifically, the proposed method reconstructs each test sample by the training data. During the reconstruction process,l₁-norm is used to generate the sparsity and different k values are used for different test samples, which solves the issue of fixed value of k. And l₂₁-norm is used to generate row sparsity which can remove noisy training samples. The experimental results on UCI datasets show that the proposed method outperforms the existing kNN classification method in terms of classification performance.

Key words: sparse learning, reconstruction, l₁-norm, l₂₁-norm, noise sample

CLC Number:

TP181

ZONG Ming, GONG Yonghong, WEN Guoqiu, CHENG Debo, ZHU Yonghua. kNN Classification Based on Sparse Learning[J].Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(3): 39-45.

References

[1] QIN Yongsong, ZHANG Shichao, ZHU Xiaofeng, et al. Semi-parametric optimization for missing data imputation[J]. Applied Intelligence, 2007,27(1):79-88. DOI: 10.1007/s10489-006-0032-0.
[2] ZHU Xiaofeng,HUANG Zi,CHENG Hong,et al.Sparse hashing for fast multimedia search[J].ACM Transactions on Information Systems,2013,31(2):9. DOI: 10.1145/2457465.2457469.
[3] ZHU Xiaofeng, LI Xuelong, ZHANG Shichao. Block-row sparse multiview multilabel learning for image classification [J].IEEE Transactions on Cybernetics, 2016, 46(2):450-461. DOI:10.1109/TCYB.2015.2403356.
[4] ZHU Xiaofeng, ZHANG Lei, HUANG Zi. A sparse embedding and least variance encoding approach to hashing [J].IEEE Transactions on Image Processing, 2014, 23(9):3737-3750. DOI:10.1109/TIP.2014.2332764.
[5] LIU Huawen,ZHANG Shichao.Noisy data elimination using mutual k-nearest neighbor for classification mining[J].Journal of Systems and Software,2012,85(5):1067-1074. DOI:10.1016/j.jss.2011.12.019.
[6] ZHU Xiaofeng,HUANG Zi,SHEN Hengtao,et al.Linear cross-modal hashing for efficient multimedia search[C]//Proceedings of the 21st ACM International Conference on Multimedia. New York: ACM Press, 2013: 143-152. DOI:10.1145/2502081.2502107.
[7] ZHU Xiaofeng,HUANG Zi,SHEN Hengtao,et al.Dimensionality reduction by mixed kernel canonical correlation analysis [J]. Pattern Recognition,2012, 45(8):3003-3016. DOI:10.1016/j.patcog.2012.02.007.
[8] HASTIE T,TIBSHIRANI R,FRIEDMAN J.统计学习基础:数据挖掘、推理与预测[M].范明,柴玉梅,咎红英,译.北京:电子工业出版社,2003:7-8.
[9] ZHU Xiaofeng,SUK H,SHEN D G.Matrix-similarity based loss function and feature selection for Alzheimer’s disease diagnosis[C]//Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA:IEEE Computer Society,2014:3089-3096. DOI:10.1109/CVPR.2014.395.
[10] ZHU Xiaofeng,HUANG Zi,YANG Yang,et al.Self-taught dimensionality reduction on the high-dimensional small-sized data[J]. Pattern Recognition,2013,46(1):215-229. DOI:10.1016/j.patcog.2012.07.018.
[11] ZHU Xiaofeng, ZHANG Shichao, JIN Zhi,et al. Missing value estimation for mixed-attribute data sets[J]. IEEE Transactions on Knowledge and Data Engineering,2011, 23(1):110-121. DOI:10.1109/TKDE.2010.99.
[12] ZHU Xiaofeng,HUANG Zi,CUI Jiangtao,et al. Video-to-shot tag propagation by graph sparse group lasso[J]. IEEE Transactions on Multimedia,2013,15(3):633-646. DOI:10.1109/TMM.2012.2233723.
[13] NESTEROV Y.Introductory lectures on convex optimization: A basic course[M]. Berlin: Springer, 2004.
[14] ZHU Xiaofeng, LI Xuelong, ZHANG Shichao,et al.Robust joint graph sparse coding for unsupervised spectral feature selection [J].IEEE Transactions on neural networks and learning systems, 2016, PP(99):1-13. DOI:10.1109/TNNLS. 2016.2521602.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

kNN Classification Based on Sparse Learning

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 3

Metrics

Comments

Recommended 0

[1]	XUE Yang,ZENG Qingke,XIA Haiying,WANG Wentao. Remote Sensing Image Fusion Based on Convolutional Neural Network Super-resolution Reconstruction [J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(2): 33-41.
[2]	DENG Zhenyun, GONG Yonghong, SUN Ke, ZHANG Jilian. A kNN Classification Algorithm Based on Local Correlation [J]. Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(1): 52-58.
[3]	SU Yi-juan, SUN Ke, DENG Zhen-yun, YIN Ke-jun. KNN Imputation Algorithm Based on LPP and l_2,1 [J]. Journal of Guangxi Normal University(Natural Science Edition), 2015, 33(4): 55-62.