常用Matlab降维软件包包括真实有效的多种降维算法： - Principal Component Analysis ('PCA') - Linear Discriminant Analysis ('LDA') - Multidimensional scaling ('MDS') - 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') - Neighborhood Preserving Embedding ('NPE') - Linearity Preserving Projection ('LPP') - Stochastic Proximity Embedding ('SPE') - Linear Local Tangent Space Alignment ('LLTSA') - Simple PCA ('SimplePCA') - Probabilistic PCA ('ProbPCA') - Conformal Eigenmaps ('CCA', implemented as an extension of LLE) - Maximum Variance Unfolding ('MVU', implemented as an extension of LLE) - Fast Maximum Variance Unfolding ('FastMVU') - Locally Linear Coordination ('LLC') - Manifold charting ('ManifoldChart') - Coordinated Factor Analysis ('CFA') - Autoencoders using RBM pretraining ('AutoEncoderRBM') - Autoencoders using evolutionary optimization ('AutoEncoderEA')