全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于局部子域的最大间距判别分析

DOI: 10.13195/j.kzyjc.2013.0100, PP. 827-832

Keywords: 线性判别分析,局部加权均值,QR分解,最大间距判别分析

Full-Text   Cite this paper   Add to My Lib

Abstract:

线性判别分析(LDA)作为一种经典的特征提取方法被广泛地加以研究和运用,然而LDA作为全局判别准则在一定程度上忽视了样本空间的局部结构和局部信息.为此,通过引入局部加权均值(LWM)并结合最大间距判别分析(MMC)提出了具有一定局部学习能力的有监督的特征提取方法—–基于局部加权均值的最大间距判别分析(LBMMC).算法结合了QR分解技术,提高了其执行效率,并通过在数据集上的测试结果表明了该算法的有效性.

References

[1]  Bian Z Q, Zhang X G. Pattern recognition[M]. Beijing: Tsinghua University, 2001.
[2]  Jolliffe I T. Principal component analysis[M]. New York: Springer-Verlag, 1986.
[3]  Todorov, Valentin, Filzmoser, et al. Comparing classical and robust sparse PCA[C]. Advances in Intelligent Systems and Computing. Germany: Springer Verlag, 2013: 1283- 291.
[4]  Wan Minghua, Lai Zhihui, Jin Zhong. Feature extraction using two-dimensional local graph embedding based on maximum margin criterion[J]. Applied Mathematics and Computation, 2011, 217(23): 9659-9668.
[5]  Vanpanik V. Statistical learning theory[M]. NewYork: Wiley, 1998.
[6]  Benabdeslem, Khalid, Hindawi, et al. Constrained Laplacian score for semi-supervised feature selection[C]. Lecture Notes in Computer Science. Germany: Springer Verlag, 2011: 204-218.
[7]  Lou Songjiang, Zhang Guoyin, Pan Haiwei, et al. Supervised Laplacian discriminant analysis for small sample size problem with its application to face recognition[J]. Computer Research and Development, 2012, 49(8): 1730-1737.
[8]  Wong W K, Zhao H T. Supervised optimal locality preserving projection[J]. Pattern Recognition, 2012, 45(1): 186-197.
[9]  Atkeson Christopher G, Moore Andrew W, Schaal Stefan. Locally weighted learning[J]. Artificial Intelligence Review, 1997, 11(1/2/3/4/5): 75-113.
[10]  Blake C L, Merz C J. UCI rrepository of machine learning databases[EB/OL]. 1998. http://www.ics.uci.edu/mlearn/MLRepository.html.
[11]  Li Rong-Hua, Liang Shuang, Baciu George, et al. Equivalence between LDA/QR and direct LDA[J]. Int J of Cognitive Informatics and Natural Intelligence, 2011, 5(1): 94-112.
[12]  Dhir Chandra Shekhar, Lee Soo-Young. Discriminant independent component analysis[J]. IEEE Trans on Neural Networks, 2011, 22(6): 845-857.
[13]  Deng Weihong, Liu Yebin, Hu Jiani, et al. The small sample size problem of ICA: A comparative study and analysis[J]. Pattern Recognition, 2012, 45(12): 4438-4450.
[14]  Yang Li-Ping, Gu Xiao-Hua, Ye Hong-Wei. Sample locality preserving discriminant analysis for classification[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2011, 19(9): 2205-2213.
[15]  Kwak, Nojun. Kernel discriminant analysis for regression problems[J]. Pattern Recognition, 2012, 45(5): 2019-2031.
[16]  Kumar Nitin, Jaiswal Ajay, Agrawal R K. Performance evaluation of subspace methods to tackle small sample size problem in face recognition[C]. ACM Int Conf on Proc Series. United States: Association for Computing Machinery, 2012: 938-944.
[17]  Shu Xin, Gao Yao, Lu Hongtao. Efficient linear discriminant analysis with locality preserving for face recognition[J]. Pattern Recognition, 2012, 45(5): 1892- 1898.
[18]  Yang Wankou, Sun Changyin, Du Helen S, et al. Feature
[19]  extraction using laplacian maximum margin criterion[J]. Neural Processing Letters, 2011, 33(1): 99-110.
[20]  Cui Yan, Fan Liya. Feature extraction using fuzzy maximum margin criterion[J]. Neurocomputing, 2012, 86(1): 52-58.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133