Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework
This work addresses matrix factorization for clustering applications, presenting an incremental improvement over existing methods.
The authors tackled semi-nonnegative matrix factorization by developing a probabilistic model based on the Skellam distribution, which outperformed classic SNMF in automatic clustering tasks on real data.
We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior components, Skellam-distributed hidden variables and observed data. Two inference algorithms are derived: Expectation-Maximization (EM) algorithm for maximum \emph{a posteriori} estimation and Variational Bayes EM (VBEM) for full Bayesian inference, including the estimation of parameters prior distribution. From this Skellam-based model, we also introduce a new divergence $\mathcal{D}$ between a real-valued target data $x$ and two nonnegative parameters $λ_{0}$ and $λ_{1}$ such that $\mathcal{D}\left(x\midλ_{0},λ_{1}\right)=0\Leftrightarrow x=λ_{0}-λ_{1}$, which is a generalization of the Kullback-Leibler (KL) divergence. Finally, we conduct experimental studies on those new algorithms in order to understand their behavior and prove that they can outperform the classic SNMF approach on real data in a task of automatic clustering.