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Matrix factorization A = B C with strictly positiv matrices B, C which minimize the reconstruction error |A - B C|. This replicate-aware version of the non-negtive matrix factorization (NMF) is based on the alternating least squares approach and exploits the replicate information to speed up the calculation.
● Data Source:
CranContrib
● Keywords:
● Alias: prismaNMF
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Efficient implementation of a replicate-aware principal component anaylsis (PCA).
● Data Source:
CranContrib
● Keywords:
● Alias: prismaDuplicatePCA
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Matrix factorization methods compress the original data matrix A in R^{f,N} with f features and N samples into two parts, namely A = B C with B in R^{f,k}, Cin R^{k, N}. The function estimateDimension estimates k based on a noise model estimated from a scrambled version of the original data matrix.
● Data Source:
CranContrib
● Keywords:
● Alias: estimateDimension
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Given a matrix factorization object A = B C, this function returns for each document the index of the inner dimension which has the maximal coordinate. Thus, it converts the fuzzy clustering found in the columns of the C matrix into a hard clustering by returning the position with the maximal coordinate value.
● Data Source:
CranContrib
● Keywords:
● Alias: getMatrixFactorizationLabels
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Loads files generated by the sally tool (see http://www.mlsec.org/sally/) and represents the data as binary token/ngrams x documents matrix. After loading, statistical tests are applied to find features which are not volatile nor constant. Co-occurring features are grouped to further compactify the data. See system.file("extdata","sallyPreprocessing.py", package="PRISMA") for a Python script which generates the corresponding .fsally file from a .sally file which reduce the loading time via loadPrismaData considerably.
● Data Source:
CranContrib
● Keywords:
● Alias: loadPrismaData
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Print and plot generic for the PRISMA objects.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.prisma, print.prisma
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Print and plot generic for the PRISMA dimension objects.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.prismaDimension, print.prismaDimension
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Print and plot generic for the PRISMA matrix factorization objects.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.prismaMF
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The PRISMA package is capable of loading and processing huge text corpora processed with the sally toolbox (http://www.mlsec.org/sally/). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.
● Data Source:
CranContrib
● Keywords: package
● Alias: PRISMA, PRISMA-package
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A matrix factorization A = B C based on the results of hclust is constructed, which holds the mean feature values for each cluster in the matrix B and the indication of the cluster in the matrix C for each data point (i.e. each data point is represented by its assigned cluster center).
● Data Source:
CranContrib
● Keywords:
● Alias: prismaHclust
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providing 
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