Local sub-domains based maximum margin criterion

被引：0

作者：

Gao, Jun ^{[1
,2
,3
]}

Huang, Li-Li ^{[2
]}

Wang, Shi-Tong ^{[4
]}

机构：

[1] School of Information Engineering, Yancheng Institute of Technology

[2] School of Electrical and Information Engineering, Anhui University of Science and Technology

[3] School of Automation, Southeast University

[4] Digital Media Institute, Jiangnan University

来源：

Kongzhi yu Juece/Control and Decision | 2014年 / 29卷 / 05期

关键词：

Linear discrimination analysis; Local weighted mean; Maximum margin criterion; QR-decomposition;

D O I：

10.13195/j.kzyjc.2013.0100

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Linear discrimination analysis (LDA) as a classic feature extraction method is widely studied and used. However, LDA as a global criterion is neglected to some extent sample space inner local structure and local information. Therefore, when combined with local weighted mean (LWM) and maximum margin criterion (MMC), a supervised feature extraction method of local learning ability, known as local sub-domains based maximum margin criterion (LBMMC), is proposed. The method is also combined with the QR decomposition technique to improve the efficiency of the algorithm. Finally, the test on the datasets show the effectiveness of the proposed method.

引用

页码：827 / 832

页数：5

共 19 条

[11]

Yang W., Sun C., Du Helen S., Et al., Feature extraction using laplacian maximum margin criterion, Neural Processing Letters, 33, 1, pp. 99-110, (2011)

[12]

Cui Y., Fan L., Feature extraction using fuzzy maximum margin criterion, Neurocomputing, 86, 1, pp. 52-58, (2012)

[13]

Wan M., Lai Z., Jin Z., Feature extraction using two-dimensional local graph embedding based on maximum margin criterion, Applied Mathematics and Computation, 217, 23, pp. 9659-9668, (2011)

[14]

Vanpanik V., Statistical Learning Theory, (1998)

[15]

Benabdeslem, Khalid, Hindawi, Et al., Constrained Laplacian score for semi-supervised feature selection, Lecture Notes in Computer Science, pp. 204-218, (2011)

[16]

Lou S., Zhang G., Pan H., Et al., Supervised Laplacian discriminant analysis for small sample size problem with its application to face recognition, Computer Research and Development, 49, 8, pp. 1730-1737, (2012)

[17]

Wong W.K., Zhao H.T., Supervised optimal locality preserving projection, Pattern Recognition, 45, 1, pp. 186-197, (2012)

[18]

Atkeson C.G., Moore A.W., Schaal S., Locally weighted learning, Artificial Intelligence Review, 11, 1-5, pp. 75-113, (1997)

[19]

Blake C.L., Merz C.J., UCI rrepository of machine learning databases, (1998)

← 1 2 →