A large-scale dynamic network (LDN) is a source of data in many big
data-related applications due to their large number of entities and large-scale
dynamic interactions. They can be modeled as a high-dimensional incomplete
(HDI) tensor that contains a wealth of knowledge about time patterns. A Latent
factorization of tensors (LFT) model efficiently extracts this time pattern,
which can be established using stochastic gradient descent (SGD) solvers.
However, LFT models based on SGD are often limited by training schemes and have
poor tail convergence. To solve this problem, this paper proposes a novel
nonlinear LFT model (MNNL) based on momentum-incorporated SGD, which extracts
non-negative latent factors from HDI tensors to make training unconstrained and
compatible with general training schemes, while improving convergence accuracy
and speed. Empirical studies on two LDN datasets show that compared to existing
models, the MNNL model has higher prediction accuracy and convergence speed.

本文提出了一种基于动量梯度下降的非线性潜在张量因子分解模型 (MNNL)，可从高维不完整张量 (HDI) 中提取非负潜在因子，改善了传统 LFT 模型的训练问题，提高了预测准确性和收敛速度。