  Matrix Factorization has many applications such as clustering. When the
matrix is Boolean it is favorable to have Boolean factors too. This will save
the efforts of quantizing the reconstructed data back, which usually is done
using arbitrary thresholds. Here we introduce topFiberM a Boolean matrix
factorization algorithm. topFiberM chooses in a greedy way the fibers (rows or
columns) to represent the entire matrix. Fibers are extended to rectangles
according to a threshold on precision. The search for these "top fibers" can
continue beyond the required rank and according to an optional parameter that
defines the limit for this search. A factor with a better gain replaces the
factor with minimum gain in "top fibers". We compared topFiberM to the
state-of-the-art methods, it achieved better quality for the set of datasets
usually used in literature. We also applied our algorithm to linked-data to
show its scalability. topFiberM was in average 128 times faster than the well
known Asso method when applied to a set of matrices representing a real
multigraph although Asso is implemented in C and topFiberM is implemented in R
which is generally slower than C. topFiberM is publicly available from Github
(https://github.com/dice-group/BMF).
