降維（子空間學習）的matlab代碼程式 Matlab codes for dimensionality reduction (subspace learning)_降維

Matlab codes for dimensionality reduction (subspace learning) If you find these algoirthms and data sets useful, we appreciate it very much if you can cite our related works:  ( Publications sort by topic )

Deng Cai, Xiaofei He, Jiawei Han, and Hong-Jiang Zhang, "Orthogonal Laplacianfaces for Face Recognition", in IEEE TIP, 2006.
Bibtex source Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. 
Bibtex source Deng Cai, Xiaofei He and Jiawei Han, "Semi-Supervised Discriminant Analysis", ICCV'07. 
Bibtex source Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han and Thomas Huang, "Learning a Spatially Smooth Subspace for Face Recognition", CVPR'07. 
Bibtex source Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces", in IEEE TPAMI, 2005.
Bibtex source Xiaofei He and Partha Niyogi, "Locality Preserving Projections", NIPS 16, 2003. 
Bibtex source Algorithms

Some general functions EuDist2: Calculate the Euclidean distance matrix of two data matrix. mySVD: An efficient SVD. NormalizeFea: Normalize the data matrix. constructW: Function used to construct the affinity matrix. constructKernel: Function used to construct the kernel matrix. PCA: Principal Component Analysis

KPCA: Kernel Principal Component Analysis

LGE: (Regularized) Linear Graph Embedding (Provides a general framework for graph based subspace learning. This function will be called by LPP, NPE, IsoProjection, LSDA, MMP ...)

OLGE: (Regularized) Orthogonal Linear Graph Embedding (Provides a general framework for graph based subspace learning (orthogonal basis vectors). This function will be called by OLPP. It is also very easy to develop ONPE, OIsoProjection, OLSDA, MMP...)

TensorLGE: Tensor Linear Graph Embedding (Provides a general framework for graph based tensor subspace learning. This function will be called by TensorLPP. It is also very easy to develop TensorNPE, TensorIsoProjection, TensorLSDA, TensorMMP...)

KGE: (Regularized) Kernel Graph Embedding (Provides a general framework for graph based kernel subspace learning. This function will be called by KernelLPP. It is also very easy to develop KernelNPE, KernelIsoProjection, KernelLSDA, KernelMMP...)

Deng Cai, Xiaofei He and Jiawei Han, "Spectral Regression for Efficient Regularized Subspace Learning", ICCV'07.
Bibtex source Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han and Thomas Huang, "Learning a Spatially Smooth Subspace for Face Recognition", CVPR'07.
Bibtex source LDA: (Regularized) Linear Discriminant Analysis (Generally, LDA can also use LGE as a subroutine. However, we can use the special graph structure of LDA to obtain some computational benefits.)

KDA: (Regularized) Kernel Discriminant Analysis (Generally, KDA can also use KGE as a subroutine. However, we can use the special graph structure of KDA to obtain some computational benefits.)

Deng Cai, Xiaofei He and Jiawei Han, "SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis", IEEE TKDE 2008. 
Bibtex source Deng Cai, Xiaofei He, Jiawei Han, "Speed Up Kernel Discriminant Analysis", The VLDB Journal, 2011. 
Bibtex source LPP: Locality Preserving Projection (You need to download LGE.m as well as constructW.m).

OLPP: Orthogonal Locality Preserving Projections (You need to download OLGE.m as well as constructW.m)

TensorLPP: Tensor Locality Preserving Projections (You need to download TensorLGE.m as well as constructW.m)

KernelLPP: Kernel Locality Preserving Projections (You need to download KGE.m as well as constructW.m)

Deng Cai, Xiaofei He, Jiawei Han, and Hong-Jiang Zhang, "Orthogonal Laplacianfaces for Face Recognition", in IEEE TIP, 2006. 
Bibtex source Xiaofei He, Deng Cai, and Partha Niyogi, "Tensor Subspace Analysis", NIPS 2005. 
Bibtex source Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces", in IEEE TPAMI, 2005. 
Bibtex source Xiaofei He and Partha Niyogi, "Locality Preserving Projections", NIPS 16, 2003. 
Bibtex source NPE: Neighborhood Preserving Embedding (You need to download LGE.m)

Xiaofei He, Deng Cai, Shuicheng Yan and Hong-Jiang Zhang, "Neighborhood Preserving Embedding," ICCV 2005. 
Bibtex source IsoProjection: Isometric Projection (You need to download LGE.m)

dijkstra.mexw32 (for 32bit Windows) 
dijkstra.mexw64 (for 64bit Windows) 
dijkstra.mexglx (for Linux): dijkstra algorithm (You can download the source code at here)

Deng Cai, Xiaofei He, and Jiawei Han, "Isometric Projection," AAAI 2007. 
Bibtex source LSDA: Locality Sensitive Discriminant Analysis (You need to download LGE.m)

Deng Cai, Xiaofei He, Kun Zhou, Jiawei Han and Hujun Bao, "Locality Sensitive Discriminant Analysis," IJCAI'07.
Bibtex source SDA: Semi-Supervised Discriminant Analysis

Deng Cai, Xiaofei He and Jiawei Han, "Semi-Supervised Discriminant Analysis", ICCV'07. 
Bibtex source

MMP: Maximum Margin Projection

Xiaofei He, Deng Cai, Jiawei Han, "Learning a Maximum Margin Subspace for Image Retrieval," TKDE 2008
Bibtex source GenSpatialSmoothRegularizer: Generate the spatially smooth regularizer 
Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han and Thomas Huang, "Learning a Spatially Smooth Subspace for Face Recognition", CVPR'07. 
Bibtex source

Return to Codes and Data
from: http://www.cad.zju.edu.cn/home/dengcai/Data/DimensionReduction.html