基于正则化的半监督等距映射数据降维方法

doi:10.11999/JEIT150694

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Abstract

This paper proposes Regularized Semi-Supervised ISOmetric MAPping (Reg-SS-ISOMAP) algorithm to solve the problem that ISOmetric MAPping (ISOMAP) algorithm is unsupervised and can not generate explicit mapping function. At first, this algorithm creates K-Connectivity Graph (K-CG) by labeled samples in training samples to get geodesic distance between approximate samples and takes it as feature vector substituting for original data. Then, it takes the geodesic distance as kernel and processes feature vector through semi-supervised regularization not MultiDimensional Scaling (MDS) algorithm. At last, it constructs objective function by regularization regression model which is low dimension and explicit mapping. The algorithm is simulated on different data sets, results show that it is stable in dimension reduction and high recognition rate.

Key words： Data dimensionality reduction Manifold learning Semi-supervised learning Regularization

Received: 08 June 2015 Published: 19 November 2015

PACS:

TP391

Fund:

Zhejiang Provincial Natural Science Foundation (LZ14F030001, LY14F030009)

Corresponding Authors: YAO Minghai E-mail: ymh@zjut.edu.cn

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	WANG Xianbao
	CHEN Shiwen
	YAO Minghai

Cite this article:

WANG Xianbao,CHEN Shiwen,YAO Minghai. Data Dimensionality Reduction Method of Semi-supervised Isometric Mapping Based on Regularization[J]. JEIT, 2016, 38(1): 241-245.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150694 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I1/241

[1]	ROWEIS S and SAUL L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326.
[2]	BELKIN M and NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 1373-1396.
[3]	KIMA Kyoungok and LEE Daewon. Inductive manifold learning using structured support vector machine[J]. Pattern Recognition, 2014, 47(1): 470-479.
[4]	杜春, 邹焕新, 孙即祥, 等. 基于改进局部切空间排列的流形学习算法[J]. 电子与信息学报, 2014, 36(2): 277-284. doi: 10.3724/SP.J.1146.2013.00135.
	DU Chun, ZOU Huanxin, SUN Jixiang, et al. Manifold learning algorithm based on modified local Tangent space alignment[J]. Journal of Electronics & Information Technology, 2014, 36(2): 277-284. doi: 10.3724/SP.J.1146. 2013.00135.
[5]	YANG Xin, FU Haoying, ZHA Hongyuan, et al. Semi- supervised nonlinear dimensionality reduction[C]. Proceedings of 32rd International Conference on Machine Learning, New York, 2006: 1065-1072.
[6]	HANSEN T J, ABRAHAMSEN T J, and HANSEN L K. Denoising by semi-supervised kernel PCA preimaging[J]. Pattern Recognition Letters, 2014, 49: 114-120.
[7]	WALDER C, HENAO R, Morten M?rup, et al. Semi-supervised kernel PCA[OL]. http://arxiv.org/abs/ 1008.1398v1.pdf, 2014. 12.
[8]	HE Xiaofei, CAI Deng, and HAN Jiawei. Learning a maximum margin subspace for image retrieval[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(2): 189-201.
[9]	CAI Deng, HE Xiaofei, and HAN Jiawei. Semi-supervised discriminant analysis[C]. Proceedings of the 11th IEEE International Conference on Computer Vision. Piscataway, 2007: 1-7.
[10]	CAI Deng, HE Xiaofei, and HAN Jiawe. Isometric projection [C]. Proceedings of 22nd Conference on Artificial Intelligence, New York, 2007: 528-533.
[11]	BELKIN M, NIYOGI P, and SANDHWANI V. Mainifold regularization: a geometric framework for learning from labeled and unlabeled examples[J]. Journal of Machine Learning Research, 2006, 7(11): 2399-2434.
[12]	方耀宁, 郭云飞, 丁雪涛, 等. 一种基于标签迁移学习的改进正则化奇异值分解推荐算法[J]. 电子与信息学报, 2013, 35(12): 3044-3050. doi: 10.3724/SP.J.1146.2013.00290.
	FANG Yaoning, GUO Yunfei, DING Xuetao et al. An improved regularized singular value decomposition recommender algorithm based on tag transfer learning[J]. Journal of Electronics & Information Technology, 2013, 35(12): 3044-3050. doi: 10.3724/SP.J.1146.2013.00290.
[13]	ZHOU Yong, LIU Beizuo, XIA Shixiong, et al. Semi- supervised extreme learning machine with manifold and pairwise constraints regularization[J]. Neurocomputing, 2015, 149: 180-186.
[14]	WANG Yunyun,?CHEN Songcan, XUE?Hui, et al. Semi- supervised classification learning by discrimination-aware manifold regularization[J]. Neurocomputing, 2015, 147: 299-306.
[15]	MAO Yu, ZHOU Yanquan, LI Ruifan, et al. Semi-supervised learning via manifold regularization[J]. The Journal of China Universities of Posts and Telecommunications, 2012, 19(6): 79-88.