prismaNMF: Matrix Factorization Based on Replicate-Aware NMF

In tammok/PRISMA: Protocol Inspection and State Machine Analysis

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.

Usage

prismaNMF(prismaData, ncomp, time = 60, pca.init = TRUE, doNorm = TRUE, oldResult = NULL)

Arguments

prismaData	PRISMA data for which a NMF should be calculated.
ncomp	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 <[e-mail]>

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

Examples

# please see the vingette for examles

tammok/PRISMA documentation built on May 31, 2019, 2:56 a.m.