Research of pattern feature extraction and selection

Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

Volumn 1, Issue , 2008, Pages 466-471

  Ding, Shi Fei ^a,b   Jia, Wei Kuan ^c   Su, Chun Yang ^a   Shi, Zhong Zhi ^b  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^c SHANDONG AGRICULTURAL UNIVERSITY   (China)

Author keywords

Independent Component Analysis; Information Theory; Manifold Learning; Neural Network; Optimization Algorithm; Pattern Feature Extraction

Indexed keywords

CONTROL THEORY; CYBERNETICS; EDUCATION; INDEPENDENT COMPONENT ANALYSIS; INFORMATION THEORY; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; OPTIMIZATION; PATTERN RECOGNITION; ROBOT LEARNING;

DEVELOPMENT TENDENCIES; HOT SPOTS; LINEAR PATTERNS; MANIFOLD LEARNING; NONLINEAR PATTERNS; PATTERN FEATURE EXTRACTION; PATTERN FEATURE EXTRACTIONS;

FEATURE EXTRACTION;

EID: 57949089674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2008.4620450     Document Type: Conference Paper

References (32)

1. Jixiang Sun, Modern pattern recognition, Defense University of Science and Technology Publishing House, Changsha, 2002.
2. Zhaoqi Bian, Xuegong Zhang. Pattern Recognition. 2nd Edition, Tsinghua University Press, Beijing, 2000.
3. Zhongzhi Shi. Knowledge Discovery. Tsinghua University Press, Beijing, 2002.
4. th, Prentice-Hall, Inc, 1998.
5. Speraman C E. General Intelligence Objectively Determined and Measured[J]. American Journal of Psychology, 1904, 15: 201-293.
6. Rui Huang, Mingyi He, ShaoJun Yang. A Margin Based Feature Extraction Algorithm for the Small Sample Size Problem. Chinese Journal of Computers, vol 30, No. 7, pp. 1173-1178, July 2007.
7. Shifei Ding, Fengxiang Jin, Zhongzhi Shi. Information Feature Compression Based on Partial Least Squares[J]. Journal of Computer Aided Design & Computer Graphics. vol 17, No. 2, pp. 368-371, Feb. 2005.
8. Wiener E, Pedersen J O, Weigend A S. A neural network approach to topic spotting. In: Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, pp. 317-332, 1995.
9. Zhong Jin, Jingyu Yang, Jianfeng Lu. An Optimal Set Of Uncorrelated Discriminant Features, Chinese Journal Of Computers, vol 22, No. 10. pp. 1105-110, Oct. 1999.
10. Aizerman M, Branverman E, Rozonoer L. Theoretical Foundations of the Potential Foundation Method in Patten Recognitions. Automation and Remote control. No. 25, pp. 821-837, 1964.
11. Jian Yang, Frangi Alejandro F, Jing-Yu Yang, et al. KPCA plus lda: A complete kernel fisher discriminant framework for feature extraction and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 27, No. 2, pp. 230-244, 2005.
12. Mika S, Retsch G, et al. Fisher discriminant analysis with kernels, In: Neural Networks for Signal Processing IX, New York: IEEE Press, pp. 41-48, 1999.
13. Ping Sun, Zongben Xu, Jianzhong Shen. Nonlinear Canonical Correlation Analysis for Discrimination Based on Kernel Methods, Chinese Journal of Computers, vol 27, No. 6, pp. 789-795, Jun. 2004.
14. Shannon C E. A mathematical theory of communication, BSTJ, pp. 379-423.1948.
15. Shizhong Li, Xiaojun Ji, Sulei Zhu. The Application Study of Entropy Analysis Method in Feature Extraction[J]. Journal of North China Institute of Technolgy, vol 20, No. 3, pp. 278-281, 1999.
16. Shifei Ding, Fengxiang Jin, Xiaoying Wang. et al. Information Feature Compression Algorithm Based on SCEC, Mini-micro Systems, vol 26, No. 7, pp. 1202-1205, July. 2005.
17. Mccllochw S, Pitts W. A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, vol 10, No. 5, pp. 115-133, 1943.
18. Zhonghua Liu, Saibin Sheng. Research on the method of fault feature extraction, Application of Electronic Technique, No. 11, pp. 19-21, 2004.
19. Johnson J L,. Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion and Intensity Signal Invariance for Images, Ap. Opt. vol 33, No. 27, pp. 6239-6253, 1994.
20. Z H Sun, Bebis G, Miller R. Object Detection Using Feature Subset Selection, Pattern Recognition, vol 37, No. 11, pp. 2165-2176, Nov. 2004.
21. Hongbin Zhang, Guangyu Sun. Tabu Search Algorithm For Feature Selection, Journal of automatization, vol 25, No. 4, pp. 457-46, Jul. 1999.
22. Fujarewicz K, Wiench M. Selecting Differentially Expressed Genes for Colon Tumor Classification, International Journal of Applied Mathematics and Computer Science, vol 13, No. 3, pp. 327-335, 2003.
23. Shifei Ding, Fengxiang Jin, Jian Wang, et. New PCA Feature Compression Algorithm Based on Information Theory, Mini-micro Systems, vol 25, No. 4, pp. 694-697, Apr. 2004.
24. Heyong Wang, Jie Zheng, Zheng'an Yao. Application of Dimension Reduction on Using Improved LLE Based on Clustering, Journal of Computer Research and Development, vol 43, No. 8, pp. 1485-1490, Aug. 2006.
25. Youping Fan, Yunping Chen, Wansheng Sun, et. Algorithm for Bi-directional Reduce Feature Data based on the Principal Component Analysis and Immune Clustering, Acta Simulata Systematica Sinica, vol 17, No. 1, pp. 148-153, Jan. 2005.
26. Jutten C, Herault J. Independent component analysis versus PCA, In: Proceeding of European Signal Processing Conf, pp. 287-314, 1988.
27. Jones M C, Sibson R. What is projection pursuit, Journal of the Royal Statistical Society, Serise. A, No. 150, pp. 1-36, 1987.
28. Seung H, Sebastian, Lee Daniel D. The Manifold Ways of Perception, Science, 290(12), pp. 2268-2269, Dec. 2000.
29. Zhuqing Yang, Yong Li, Dewen Hu. Independent Component Analysis: A Survey, Journal of automatization, vol 28, No. 5, pp. 762-772, Sep. 2002.
30. Fengxi Song, Xiumei Gao, ShuHai Liu. Dimensionality Reduction in Statistical Pattern Recognition and Low Loss Dimensionality Reduction, Chinese Journal of Computers, vol 28, No. 11, pp. 1915-1922, Nov. 2005.
31. Nguyen H S, Skowron A, Searching for features defined by hyperplanes, In Z W Ras, M Michalewicz (eds). Proc. of the IX International Symposium Methodologies for Information Systems, Polang, Kopane, 1996.
32. Shen D. D is criminative wavelet shape descriptors for recognition of 2-D patterns, Pattern Recognition, vol 32, No. 2, pp. 151-165, 1999.