SubspaceLearningCodes
所属分类：数值算法/人工智能
开发工具：matlab
文件大小：169KB
下载次数：50
上传日期：2009-09-11 08:53:15
上 传 者：
dourongrong
说明： 子空间学习的代码，主要包括人脸识别中常用的特征提取算法如pca lda 以及目前常见的流行学习的相关代码
(Subspace learning the code, mainly including commonly used in face recognition feature extraction algorithms such as pca lda and the current prevalence of common learning-related code)
文件列表:
Subspace Learning Codes\constructW.m (10630, 2007-01-08)
Subspace Learning Codes\LDA.m (2239, 2007-01-08)
Subspace Learning Codes\LPP.m (5484, 2007-01-08)
Subspace Learning Codes\OLPP.m (6620, 2007-01-08)
Subspace Learning Codes\PCA.m (6216, 2007-01-08)
Subspace Learning Codes\TensorLPP.m (5691, 2007-01-08)
Subspace Learning Codes\Yale.mat (169368, 2007-01-08)
Subspace Learning Codes (0, 2009-02-19)
Q & A:
Q: How to use the code?
A: You need to put LPP.m, PCA.m in your working directory. If you want to use constrctW to
construct the affinity matrix, you should also put constructW.m in your working directory.
'help LPP' for detail information.
'help constructW' for details on how to construct the affinity matrix.
Q: Why LPP code does not work in my application?
A: If the number of samples is much larger than the number of features, you might need to use the
Kernel LPP.
The affinity graph construction plays a key role, you might need to constuct the affinity
matrix by your own (based on your knowledge of your application). Please refer the following
papers.
Reference:
Xiaofei He, and Partha Niyogi, "Locality Preserving Projections"
Advances in Neural Information Processing Systems 16 (NIPS 2003),
Vancouver, Canada, 2003.
Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang Zhang,
"Face Recognition Using Laplacianfaces", IEEE PAMI, Vol. 27, No. 3, Mar. 2005.
Deng Cai, Xiaofei He and Jiawei Han, "Document Clustering Using Locality Preserving Indexing"
IEEE TKDE, 2005.
Deng Cai, Xiaofei He and Jiawei Han, "Using Graph Model for Face Analysis",
Technical Report, UIUCDCS-R-2005-2636, UIUC, Sept. 2005
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