R: Matrix Factorization Based on Replicate-Aware NMF
prismaNMF
R Documentation
Matrix Factorization Based on Replicate-Aware NMF
Description
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.
either an integer or prismaDimension object specifying
the inner dimension of the matrix factorization.
time
seconds after which the calculation should end.
pca.init
should the B matrix be initialized by a PCA.
doNorm
should the B matrix normalized (i.e. all columns have the
Euclidean length of 1).
oldResult
re-use results of a previous run, i.e. B and C are
pre-initialized with the values of this previous matrix
factorization object.
Value
prismaNMF
Matrix factorization object containing the B and
C matrix.
Author(s)
Tammo Krueger <tammokrueger@googlemail.com>
References
Krueger, T., Gascon, H., Kraemer, N., Rieck, K. (2012)
Learning Stateful Models for Network Honeypots
5th ACM Workshop on Artificial Intelligence and Security (AISEC 2012), accepted
R. Albright, J. Cox, D. Duling, A. Langville, and C. Meyer. (2006)
Algorithms, initializations, and convergence for the nonnegative
matrix factorization. Technical Report 81706, North Carolina State University