This Matlab toolbox implements 32 techniques for dimensionality reduction. These techniques are all available through the COMPUTE_MAPPING function. The following techniques are available:
- Principal Component Analysis ('PCA')
- Linear Discriminant Analysis ('LDA')
- Multidimensional scaling ('MDS')
- Simple PCA ('SimplePCA')
- 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')
- Stochastic Neighbor Embedding ('SNE')
- Symmetric Stochastic Neighbor Embedding ('SymSNE')
- t-Distributed Stochastic Neighbor Embedding ('tSNE')
- Neighborhood Preserving Embedding ('NPE')
- Linearity Preserving Projection ('LPP')
- Stochastic Proximity Embedding ('SPE')
- Linear Local Tangent Space Alignment ('LLTSA')
- Conformal Eigenmaps ('CCA', implemented as an extension of LLE)
- Maximum Variance Unfolding ('MVU', implemented as an extension of LLE)
- Landmark Maximum Variance Unfolding ('LandmarkMVU')
- Fast Maximum Variance Unfolding ('FastMVU')
- Locally Linear Coordination ('LLC')
- Manifold charting ('ManifoldChart')
- Coordinated Factor Analysis ('CFA')
- Autoencoders using stack-of-RBMs pretraining ('AutoEncoderRBM')
- Autoencoders using evolutionary optimization ('AutoEncoderEA')
(quote from: Mr. Laurens van der Maaten home page)
hollyboy 2009年08月07日[编辑]