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Table 2 NMF-based data mining approaches

From: The non-negative matrix factorization toolbox for biological data mining

Function Description
NMFCluster Take the coefficient matrix produced by a NMF algorithm, and output the clustering result.
chooseBestk Search the best number of clusters based on dispersion Coefficients.
biCluster The biclustering method using one of the NMF algorithms.
featureExtractionTrain General interface. Using training data, generate the bases of the NMF feature space.
featureExtractionTest General interface. Map the test/unknown data into the feature space.
featureFilterNMF On training data, select features by various NMFs.
featSel Feature selection methods.
nnlsClassifier The NNLS classifier.
perform Evaluate the classifier performance.
changeClassLabels01 Change the class labels to be in {0,1,2,,C−1} for C-class problem.
gridSearchUniverse A framework to do line or grid search.
classificationTrain Train a classifier, many classifiers are included.
classificationPredict Predict the class labels of unknown samples via the model learned by classificationTrain.
multiClassifiers Run multiple classifiers on the same training data.
cvExperiment Conduct experiment of k-fold cross-validation on a data set.
significantAcc Check if the given data size can obtain significant accuracy.
learnCurve Fit the learning curve.
FriedmanTest Friedman test with post-hoc Nemenyi test to compare multiple classifiers on multiple data sets.
plotNemenyiTest Plot the CD diagram of Nemenyi test.
NMFHeatMap Draw and save the heat maps of NMF clustering.
NMFBicHeatMap Draw and save the heat maps of NMF biclustering.
plotBarError Plot Bars with STD.
writeGeneList Write the gene list into a.txt file.
normmean0std1 Normalization to have mean 0 and STD 1.
sparsity Calculate the sparsity of a matrix.
MAT2DAT Write a data set from MATLAB into.dat format in order to be readable by other languages.