Concept lattice based recognition method of celestial spectra outliers

Zidonghua Xuebao/ Acta Automatica Sinica

Volumn 34, Issue 9, 2008, Pages 1060-1066

  Zhang, Ji Fu ^a   Jiang, Yi Yong ^a   Hu, Li Hua ^a   Cai, Jiang Hui ^a   Zhang, Su Lan ^a  

^a TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Celestial spectra; Concept lattice; Dense coefficient; Outliers; Sparsity subspace

Indexed keywords

CELESTIAL SPECTRA; CONCEPT LATTICE; DENSE COEFFICIENT; OUTLIERS; SPARSITY SUBSPACE;

TWO PHASE FLOW;

EID: 53649089614     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1004.2008.01060     Document Type: Article

References (16)

1. Qin Dong-Mei. Studies on Automated Spectral Recognition of Celestial Object [Ph. D. dissertation], Institute of Automation, Chinese Academy of Sciences, 2003.
2. Zhang Ji-Pu, Cai Jiang-Hui. A study on the outlier mining system for LAMOST spectra. Spectroscopy and Spectral Analysis, 2007, 27(3): 606-609.
3. Duan Pu-Qing. Studies on Automated Spectral Processing of Celestial Objects Based on Template Matching [Ph. D. dissertation], Institute of Automation, Chinese Academy of Sciences, 2006.
4. Liu Zhong-Tian, Li Xiang-Ru, Wu Pu-Chao, Zhao Yong-Heng. A method for auto-recognizing the M-type stars based on wavelet feature. Acta Electronica Sinica, 2007, 35(1): 157-160.
5. Knorr E M, Ng R T. Algorithms formining distance-based outliers in large datasets. In: Proceedings of the 24th International Conference on Very Large Data Bases. San Francisco, USA: Morgan Kaufmann Publishers, 1998. 392-403.
6. Han J W, Kamber M. Data Mining Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers, 2001.
7. Barnett V, Lewis T. Outliers in Statistical Data. New York: John Wiley-Sons, 1994.
8. Arning A, Agrawal R, Raghavan P. A linear method for deviation detection in large database. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portlan, Oregon: Morgan Kaufmann Publishers, 1996. 164-169.
9. Breunig M M, Kriegel H P, Ng R T, Sander J. LOF: identifying density-based local outliers. ACM Special Interest Group on Management of Data Record, 2000, 29(2): 93-104.
10. Agarwal C, Yu S. An effective and efficient algorithm for high-dimensional outlier detection. The International Journal on Very Large Data Bases, 2005, 14(2): 211-221.
11. Wille R. Restructuring lattice theory: an approach based on hierarchies of concepts. Ordered Sets, 1982, 11(5): 445-470.
12. Adam D T, Zhang G Q, Tian Y. Faster concept analysis. Lecture Notes in Computer Science, 2007, 4604: 206-219.
13. Godin R, Missaoue R, Alaui H. Incremental concept formation algorithms based on galois (concept) lattice. Computational Intelligence, 1995, 11(2): 246-267.
14. Jiang Yi-Yong, Zhang Ji-Fu, Zhang Su-Lan. Incremental construction of concept lattice based on linked list structure. Computer Engineering and Applications, 2007, 43(11): 178-180.
15. Li Xiang-Ru. Pattern Recognition Methods and Its Application in Spectral Classification of Galaxy [Ph. D. dissertation], Institute of Automation, Chinese Academy of Sciences, 2006.
16. Xie Zhi-Peng. Knowledge Discovery Based on Concept Lattice Research [Ph. D. dissertation], Hefei University of Technology, 2001.