Manifold regularization based semi-supervised regression on multivariate time series

Zhongguo Kuangye Daxue Xuebao/Journal of China University of Mining and Technology

Volumn 40, Issue 3, 2011, Pages 492-498

  Zhao, Zhi Kai ^a   Qian, Jian Sheng ^a   Cheng, Jian ^a   Li, Xiao Bin ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

Manifold learning; Multivariate time series; Regularization; Semi supervised regression

Indexed keywords

EDGE WEIGHTS; GRAPH LAPLACIAN; MANIFOLD LEARNING; MULTIVARIATE SENSORS; MULTIVARIATE TIME SERIES; PUBLIC DATASET; REGRESSION ALGORITHMS; REGULARIZATION; REGULARIZATION FRAMEWORK; REPRESENTER THEOREM; SEMI-SUPERVISED; SPATIAL INFORMATIONS; TEMPORAL INFORMATION; TEMPORAL RELATION; TIME-SERIES DATA;

LINEAR TRANSFORMATIONS; TIME SERIES;

REGRESSION ANALYSIS;

EID: 79958234263     PISSN: 10001964     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. CHAPELLE O, SCHOLKOPF B, ZIEN A. Semi-supervised learning[M]. Cambridge: MIT Press, 2006.
2. ZHU Xiao-jin. Semi-supervised learning with graphs[D]. Pittsburgh: School of Computer Science, Carnegie Mellon University, 2005.
3. BELKIN M, NIYOGI P. Semi-supervised learning on riemannian manifolds[J]. Machine Learning, 2004, 56(1/3): 209-239.
4. 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(11): 2399-2434.
5. SINDHWANI V, NIYOGI P, BELKIN M. Beyond the point cloud: from transductive to semi-supervised learning[C]//Proceedings of the 22nd International Conference on Machine Learning. New York: ACM Press, 2005: 824-831.
6. YANG Jian, WANG Jue, ZHONG Ning. Laplacian semi-supervised regression on a manifold[J]. Journal of Computer Research and Development, 2007, 44(7): 1121-1127.
7. POZDNOUKHOV A, BENGIO S. Semi-supervised kernel methods for regression estimation[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Toulouse: Institute of Electrical and Electronics Engineers, 2006: 577-580.
8. JENKINS O C, MATARIC M J. A spatio-temporal extension to isomap nonlinear dimension reduction[C]//Proceedings of the 21st International Conference on Machine Learning. New York: ACM Press, 2004: 441-448.
9. YANG Hui-hua, FENG Qin, QIONG Lin-liang, et al. Laprlsr for nir spectral modeling and its application to online monitoring of the column separation of salvianolate[J]. Chinese Chemical Letters, 2007, 18(7): 852-856.
10. PHILIP H F, DICK V D. Non-linear time series models in empirical finance[M]. Cambridge: Cambridge University Press, 2000: 6-14.
11. ZHANG Jian-ying, CHENG Jian, HOU Yu-hua, et al. Forecasting coalmine gas concentration based on adaptive neuro-fuzzy inference system[J]. Journal of China University of Mining and Technology, 2007, 36(4): 494-498.