An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent
  Factor Analysis
Jinli Li Ye Yuan
High-dimensional and incomplete (HDI) matrix contains many complex
interactions between numerous nodes. A stochastic gradient descent (SGD)-based
latent factor analysis (LFA) model is remarkably effective in extracting
valuable information from an HDI matrix. However, such a model commonly
encounters the problem of slow convergence because a standard SGD algorithm
only considers the current learning error to compute the stochastic gradient
without considering the historical and future state of the learning error. To
address this critical issue, this paper innovatively proposes an
ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error
by considering the historical and future state by following the principle of an
ADRC controller. With it, an ADS-based LFA model is further achieved for fast
and accurate latent factor analysis on an HDI matrix. Empirical studies on two
HDI datasets demonstrate that the proposed model outperforms the
state-of-the-art LFA models in terms of computational efficiency and accuracy
for predicting the missing data of an HDI matrix.