ROBUST BINARY COMPONENT DECOMPOSITIONS
Christos Kolomvakis (University of Mons); Nicolas Gillis (Université de Mons)
-
SPS
IEEE Members: $11.00
Non-members: $15.00
Semi-binary matrix factorization (semi-BMF) is a matrix de-
composition model where the elements of one factor are bi-
nary. Semi-BMF can be interpreted as a generalization of k-
means, and can be employed in clustering problems such as
community detection. In the absence of noise, Kueng and
Tropp (SIAM J. Math. Data Sc., 2021) have recently pro-
posed a provably correct algorithm for semi-BMF that require
to solve semidefinite programs (SDPs). In this paper, we ex-
tend their approach in the presence of noise. Moreover, since
standard solvers for SDP rely on interior-point methods and
do not scale well, we also propose a first-order method to re-
duce the computational costs. We test our new algorithms on
synthetic data, and show that they compare favorably with the
state of the art.