一种基于流形正则化的半监督指纹定位算法

doi:10.13474/j.cnki.11-2246.2018.0035

测绘通报 ›› 2018, Vol. 0 ›› Issue (2): 11-15,20.doi: 10.13474/j.cnki.11-2246.2018.0035

一种基于流形正则化的半监督指纹定位算法

朱顺涛^1,2, 卢先领^1,2, 于丹石²

1. 江南大学"轻工过程先进控制"教育部重点实验室, 江苏无锡 214100;
2. 江南大学物联网工程学院, 江苏无锡 214100

收稿日期:2017-11-27 出版日期:2018-02-25 发布日期:2018-03-06
作者简介:朱顺涛(1993-),男,硕士生,主要研究方向为室内定位、机器学习。E-mail:6151904012@vip.jiangnan.edu.cn
基金资助:
江苏省产学研联合创新资金前瞻性联合研究项目（BY2014023-31）；江苏省“六大人才高峰”项目（WLW-007）

A Semi-supervised Fingerprint Localization Algorithm Based on Manifold Regularization

ZHU Shuntao^1,2, LU Xianling^1,2, YU Danshi²

1. Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University, Wuxi 214100, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214100, China

Received:2017-11-27 Online:2018-02-25 Published:2018-03-06

摘要/Abstract

摘要：

针对传统指纹定位算法采集带标签训练数据成本高的问题，本文提出了一种基于流形正则化的半监督指纹定位算法。首先以流形假设为依据，利用批量输入的带标签数据与无标签数据之间的相似度构建图拉普拉斯算子；然后与极限学习机算法相结合，通过随机特征映射建立隐含层；最后在流形正则化框架下，求解隐含层和输出层之间的权值矩阵，从而建立位置估计模型。仿真结果表明，与INN、SVR、ELM 3种算法相比，该算法的训练和测试时间相对较短，且在带标签训练数据稀疏的前提下仍能保持较高的准确率与稳定性。

关键词: 指纹定位, 半监督学习, 流形正则化, 极限学习机, 图拉普拉斯算子

Abstract:

Due to traditional fingerprint localization algorithms cost too much on collecting training dataset with labels,a new semi-supervised fingerprint localization algorithm based on manifold regularization is proposed.Firstly,the graph laplacian is initialized with the labeled and unlabeled training dataset under the manifold hypothesis.Subsequently,combined with the extreme learning machine,the hidden layer is constructed by using random mapping.Finally,the output weighted matrix between the hidden layer and output layer is solved under the framework of manifold regularization,so that the location estimated model is established.The simulation results show that,compared with INN,SVR and ELM,the proposed algorithm need shorter training time and testing time.In addition,it can maintain the high accuracy and reliability while labeled training dataset is sparse.

Key words: fingerprint localization, semi-supervised learning, manifold regularization, extreme learning machine, graph Laplacian

中图分类号:

P228

朱顺涛, 卢先领, 于丹石. 一种基于流形正则化的半监督指纹定位算法[J]. 测绘通报, 2018, 0(2): 11-15,20.

ZHU Shuntao, LU Xianling, YU Danshi. A Semi-supervised Fingerprint Localization Algorithm Based on Manifold Regularization[J]. 测绘通报, 2018, 0(2): 11-15,20.

参考文献

[1] DENG Zhongliang,YU Yanpei,YUAN Xie,et al.Situation and Development Tendency of Indoor Positioning[J].China Communication,2013,10(3):42-55.
[2] 靳超,邱冬炜.基于WiFi信号室内定位技术的研究[J].测绘通报,2017(5):21-25.
[3] 毕京学,甄杰,汪云甲,等.高斯函数定权的改进KNN室内定位方法[J].测绘通报,2017(6):9-12.
[4] HE S,CHAN S H G.Wi-Fi Fingerprint-based Indoor Positioning:Recent Advances and Comparisons[J].IEEE Communications Surveys & Tutorials,2016,18(1):466-490.
[5] 崔斌,赵西安.一种基于传播模型和位置指纹的混合室内定位方法[J].测绘通报,2015(6):35-43.
[6] XIA S,LIU Y,Yuan G,et al.Indoor Fingerprint Positioning Based on Wi-Fi:An Overview[J].International Journal of Geo-Information,2017,6(5):135.
[7] MOK E,CHEUNG B K S.An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning[J].ISPRS International Journal of Geo-Information,2013,2(3):854-868.
[8] SHI K,CHEN H S,ZHANG R T.Indoor Location Method Based on Support Vector Regression in 802.11 Wireless Rnvironments[J].Journal of Software,2014,25(11):2636-2651.
[9] DWIYASA F,LIM M H,ONG Y S,et al.Extreme Learning Machine for Indoor Location Fingerprinting[J].Multidimensional Systems & Signal Processing,2017,28(3):867-883.
[10] 向铭.基于半监督学习的室内WLAN支持向量回归定位算法[D].重庆:重庆邮电大学,2016.
[11] PULKKINEN T,ROOS T,MYLLYMÄKI P.Semi-supervised Learning for WLAN Positioning[C]//Proceedings of the 21th International Conference on Artificial Neural Networks and Machine Learning.Espoo:Springer-Verlag,2011.
[12] HUANG G B,ZHOU H,DING X,et al.Extreme Learning Machine for Regression and Multiclass Classification[J].IEEE Transactions on Systems,Man and Cybernetics,2012,42(2):513-529.
[13] LU X,ZOU H,ZHOU H,et al.Robust Extreme Learning Machine With its Application to Indoor Positioning[J].IEEE Transactions on Cybernetics,2016,46(1):194-205.
[14] NIYOGI P,Manifold Regularization and Semi-supervised Learning:Some Theoretical Analyses[J].Journal of Machine Learning Research,2013,14(1):1229-1250.
[15] BELKIN M,NIYOGI P,SINDHWANI V.Manifold Regularization:A Geometric Framework for Learning from Labeled and Unlabeled Examples[J].Journal of Machine Learning Research,2006,7(1):2399-2434.
[16] 陶新民,曹盼东,宋少宇,等.基于半监督高斯混合模型核的支持向量机分类算法[J].信息与控制,2013,42(1):18-26.

一种基于流形正则化的半监督指纹定位算法

A Semi-supervised Fingerprint Localization Algorithm Based on Manifold Regularization

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