Search - probabilistic principal component analysis matlab

Search - probabilistic principal component analysis matlab - List

[Algorithm] ppca DL : 0: Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.-Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA (X, PPCA_DIM) computes the principal component subspace U of dimension PPCA_DIM using a centred covariancematrix X. The variable VAR contains the off-subspace variance (whichis assumed to be spherical ), while the vector LAMBDA contains thevariances of each of the principal components. This is computedusing the eigenvalue and eigenvector decomposition of X.
Update : 2025-02-17 Size : 1kb Publisher : 西晃云
[Special Effects] drtoolbox.tar DL : 1: 这是一个MATLAB工具箱包括32个降维程序，主要包括 pca,lda,MDS等十几个程序包，对于图像处理非常具有参考价值- ，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 )
Update : 2025-02-17 Size : 1.06mb Publisher : yang
[matlab] ACM_matlab DL : 0: 用于主成分分析和概率主成分分析的Matlab程序-Used principal component analysis and probabilistic principal component analysis Matlab program
Update : 2025-02-17 Size : 1.02mb Publisher : 罗宾汉
[AI-NN-PR] drtoolbox DL : 0: Matlab针对各种数据预处理的降维方法，源码集合。-Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques: Principal Component Analysis (PCA) Probabilistic PCA Factor Analysis (FA) Sammon mapping Linear Discriminant Analysis (LDA) Multidimensional scaling (MDS) Isomap Landmark Isomap Local Linear Embedding (LLE) Laplacian Eigenmaps Hessian LLE Local Tangent Space Alignment (LTSA) Conformal Eigenmaps (extension of LLE) Maximum Variance Unfolding (extension of LLE) Landmark MVU (LandmarkMVU) Fast Maximum Variance Unfolding (FastMVU) Kernel PCA Generalized Discriminant Analysis (GDA) Diffusion maps Stochastic Neighbor Embedding (SNE) Symmetric SNE (SymSNE) new: t-Distributed Stochastic Neighbor Embedding (t-SNE) Neighborhood Preserving Embedding (NPE) Locality Preserving Projection (LPP) Linear Local Tangent Space Alignment (LLTSA) Stochastic Proximity Embedding (SPE) Mu
Update : 2025-02-17 Size : 1.94mb Publisher : jdzsj
[Special Effects] drtoolbox.tar DL : 0: 用于降维的matlab工具包，包括PCA，LDA，LLE，等-Matlab Toolbox for Dimensionality Reduction Principal Component Analysis (PCA) Probabilistic PCA Factor Analysis (FA) Classical multidimensional scaling (MDS) Sammon mapping Linear Discriminant Analysis (LDA) Isomap Landmark Isomap Local Linear Embedding (LLE) Laplacian Eigenmaps
Update : 2025-02-17 Size : 1.07mb Publisher : 晗嫣