Momentum-Incorporated Symmetric Non-Negative Latent Factor Models

Symmetric high-dimensional and sparse (SHiDS) networks are frequently found in various industrial applications. A symmetric non-negative latent factor (SNLF) model can acquire essential features from them precisely, yet it suffers from slow convergence. To address this issue, this article integrates a generalized momentum method into a symmetric, single latent factor-dependent, non-negative and multiplicative update (S
²
LF-NMU) algorithm, thereby achieving a
m
omentum-incorporated,
s
ymmetric,
s
ingle-latent-factor-dependent
n
on-negative-multiplicative-update (MS
²
N) algorithm. Based on an MS
²
N algorithm, momentum-incorporated symmetric non-negative latent factor (MSNLF) models are proposed for an SHiDS network, which ensures fast convergence as well as high representative learning ability. Empirical studies on four SHiDS networks from industrial applications demonstrate that compared with state-of-the-art models, the proposed MSNLF models have significantly higher computational efficiency and representative learning ability.