Description: ，This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function or trhough the GUI. The following techniques are available:
- Principal Component Analysis ( PCA )
- Linear Discriminant Analysis ( LDA )
- Multidimensional scaling ( MDS )
- Probabilistic PCA ( ProbPCA )
- Factor analysis ( FactorAnalysis )
- Sammon mapping ( Sammon )
- Isomap ( Isomap )
- Landmark Isomap ( LandmarkIsomap )
- Locally Linear Embedding ( LLE )
- Laplacian Eigenmaps ( Laplacian )
- Hessian LLE ( HessianLLE )
- Local Tangent Space Alignment ( LTSA )
- Diffusion maps ( DiffusionMaps )
- Kernel PCA ( KernelPCA )
- Generalized Discriminant Analysis ( KernelLDA )
- [lle] - This algorithm is an improved algorithm
- [PCA] - PCA algorithm Matlab examples detailed C
- [malic] - Malic is a complete face recognition sys
- [KPCA] - kernel PCA
- [pcajiafisher] - pca+ fisher is the kernel function is ap
- [lda] - Nonlinear dimensionality reduction metho
- [KPCA] - PCA is not suitable to address the many
- [A_Study_og_Face_Recognition_Methods_Baced_on_Wavel] - For gray-scale images, a knowledge-based
- [LDA] - A CLASSICAL LINEAR DISCRIMINANT ANALYSIS
- [KernelLLE] - Kernel LLE control chart
File list (Check if you may need any files):
5956477drtoolbox.tar
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