Home Technical Report Software Datasets Demo
ManifoldLearn Matlab Routines for Manifold RegularizationContact: Vikas Sindhwani (firstname.lastname@example.org)
ManifoldLearn implements Laplacian Regularized Least Squares and Laplacian Support Vector Machines algorithms. Clustering, semi-supervised and fully supervised learning tasks can be handled within the same conceptual and algorithmic framework.
This software was tested with matlab 6.5 R13 running under Linux. Also tested under windows running cygwin (Put cygwin1.dll in your windows path or invoke matlab from cygwin; mex files were compiled under cygwin using gnumex ).
Download Current Version (June 2004) : ManifoldLearn.tar.gz
Usage: Here are some examples to get started. Type "help filename" for more information.
Generate an options structure for training
Train a Classifier
Test a classifier
[real_output, binary_labels, error_rate]=ml_test(classifier, Xtest, Ytest);
K=calckernel('rbf', 1, X);
calckernel computes linear, polynomial or RBF kernels.
Computing Graphs Laplacian
Graphs can be constructed with euclidean or cosine distance measures; with binary weights or heat kernel weights; normalized or unnormalized.
Implemented in svm.m, rlsc.m, lapsvm.m, laprlsc.m -- these routines take gram matrix and graph laplacian matrix, and output expansion coefficients (alphas). A mex interface to libsvm is used for SVM QP.
Can use ROC curves, precision-recall breakeven points, etc. Uses some of Jason Rennie's Matlab Code.