Marginal Discriminant Projection for Coal Mine Safety Data Dimensionality Reduction

• Autorzy: Zhao Z. , Qian J. , Cheng J.

Warianty tytułu

PL

Metoda Marginal Discriminant Projection w zastosowaniu do redukcji wymiaru danych w systemach bezpieczeństwa kopalni węglowych

• Języki publikacji: EN

Abstrakty

EN

Marginal Fisher Analysis (MFA) is a novel dimensionality reduction algorithm. However, the two nearest neighborhood parameters are difficult to select when constructing graphs. In this paper, we propose a nonparametric method called Marginal Discriminant Projection (MDP) which can solves the problem of parameters selection in MFA. Experiment on several benchmark datasets demonstrated the effectiveness of our proposed method, and appreciate performance was achieved when applying on coal mine safety data dimensionality reduction.

PL

W artykule zaproponowano nieparametryczna metodę nazwaną MDOP (marginal discriminant projection) która pomaga rozwiązać problem selekcji danych w algorytmie MFA (marginal Fisher analysis). Metodę zastosowano do redukcji danych w systemach bezpieczeństwa klopalni węglowych.

Słowa kluczowe

EN

coal mine safety; dimension reduction; marginal discriminant projection

PL

kopalnia węgla; metoda MFA; redukcja wymiaru

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 9b
• Strony: 79--83
• Opis fizyczny: Bibliogr. 14 poz., rys., wykr.

Twórcy

autor

Zhao Z.

• China University of Mining and Technology

autor

Qian J.

• China University of Mining and Technology

autor

Cheng J.

• China University of Mining and Technology

Bibliografia

• [1] Y. Zeng, C. Wu. Research on fuzzy fractal neural network for prediction of mine gas emission. Coal Science and Technology. vol.32, no.2. pp.62-65,2004.
• [2] C. Cai, G. Jing. Fuzzy Synthetic Evaluation of Prediction of Coal and Gas Outburst Scale. Journal of Safety and Environment. Vol.4, no.2, pp.54-56,2004.
• [3] N. Li, D. Guo, M. Fan. Ash related analysis method applied to analysis of coal and gas outburst controlled factors. Coal Science and Technology. vol.32, no.2. pp.67-69,2004.
• [4] J. Cheng, J. Bai, J. Qian and S. Li. Short- Term Forecasting Method of Coalmine Gas Concertration Based on Chaotic Time Series. Journal of China University of Mining & Technology. vol.37, no.2. pp.231-235, 2008.
• [5] J. Zhang, J. Cheng, J. Qian, Forecasting Coalmine Gas Concentration Based on Adaptive Neuro-Fuzzy Inference System. Journal of China University of Mining & Technology. vol.36, no.4. pp.494-498, 2007.
• [6] Y. Hou, J. Cheng, S. Li. Forecasting coalmine gas concentration based on RBF neural network. Proceedings of the International Conference on Information Acquisition. pp.192-194, 2007.
• [7] A. M. Artinez and A. C. Kak, “PCA Versus LDA”. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp.228-233, 2001.
• [8] X. He and P. Niyogi. “Locality preserving projection”. In Advances in Neural Information Processing System 16. MIT Press, Massachusetts, 2003.
• [9] S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang and S. Lin. “Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. Pattern Analysis and Machine Intelligence, IEEE Transactions on”, vol. 29, no. 1, pp. 40-51, 2007.
• [10] J. Xu, J. Yang. “Nonparametric Marginal Fisher Analysis for Feature Extraction“. Lecture Notes in Computer Science, Vol. 6216, pp. 221-228, 2010.
• [11] D. Xu , S. Yan , D. Tao, S. Lin and H. Zhang. “Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval“. IEEE transactions on image processing, vol. 16, no. 11, pp.2811-2821, 2007.
• [12] J. Hu, W. Deng, J. Guo and W. Xu. “Learning a locality discriminating projection for classification. Knowledge-Based System, vol.22, no.8, pp.562-568, 2009.
• [13] Z. Zeng, C.Liu, L. Huang. “Analysis on two Fisher Methods and a Synthesized Discriminant Projection”. Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. No. (8-11), pp.1-4, 2008.
• [14] E. Kokiopoulou, Y. Saad. “Enhanced graph-based dimensionality reduction with repulsion Laplaceans”. Pattern Recognition. vol. 42, no. 11, pp. 2392-2402, 2009.