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Dimensionality Reduction

siniorcodeforge
2014-09-18 22:06:24
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Description

This Matlab toolbox implements 34 techniques for dimensionality reduction and metric learning.

 These techniques are all available through the COMPUTE_MAPPING function or through 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')
 - Stochastic Neighbor Embedding ('SNE')
 - Symmetric Stochastic Neighbor Embedding ('SymSNE')
 - t-Distributed Stochastic Neighbor Embedding ('tSNE')
 - Neighborhood Preserving Embedding ('NPE')
 - Locality 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')
 - Gaussian Process Latent Variable Model ('GPLVM')
 - Deep autoencoders ('Autoencoder')
 - Neighborhood Components Analysis ('NCA')
 - Maximally Collapsing Metric Learning ('MCML')
 - Large Margin Nearest Neighhbor metric learning ('LMNN')


Furthermore, the toolbox contains 6 techniques for intrinsic dimensionality estimation. These techniques are available through the function INTRINSIC_DIM. The following techniques are available:


 - Eigenvalue-based estimation ('EigValue')
 - Maximum Likelihood Estimator ('MLE')
 - Estimator based on correlation dimension ('CorrDim')
 - Estimator based on nearest neighbor evaluation ('NearNb')
 - Estimator based on packing numbers ('PackingNumbers')
 - Estimator based on geodesic minimum spanning tree ('GMST')


In addition to these techniques, the toolbox contains functions for prewhitening of data (the function PREWHITEN), exact and estimate out-of-sample extension (the functions OUT_OF_SAMPLE and OUT_OF_SAMPLE_EST), and a function that generates toy datasets (the function GENERATE_DATA).
The graphical user interface of the toolbox is accessible through the DRGUI function.

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Name Size Date
<>0.00 B2012-04-18 07:57
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Dimensionality Reduction (1.07 MB)

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