A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor
  Analysis
Yurong Zhong Zhe Xie Weiling Li Xin Luo
High-Dimensional and Incomplete (HDI) data is commonly encountered in big
data-related applications like social network services systems, which are
concerning the limited interactions among numerous nodes. Knowledge acquisition
from HDI data is a vital issue in the domain of data science due to their
embedded rich patterns like node behaviors, where the fundamental task is to
perform HDI data representation learning. Nonnegative Latent Factor Analysis
(NLFA) models have proven to possess the superiority to address this issue,
where a linear bias incorporation (LBI) scheme is important in present the
training overshooting and fluctuation, as well as preventing the model from
premature convergence. However, existing LBI schemes are all statistic ones
where the linear biases are fixed, which significantly restricts the
scalability of the resultant NLFA model and results in loss of representation
learning ability to HDI data. Motivated by the above discoveries, this paper
innovatively presents the dynamic linear bias incorporation (DLBI) scheme. It
firstly extends the linear bias vectors into matrices, and then builds a binary
weight matrix to switch the active/inactive states of the linear biases. The
weight matrix's each entry switches between the binary states dynamically
corresponding to the linear bias value variation, thereby establishing the
dynamic linear biases for an NLFA model. Empirical studies on three HDI
datasets from real applications demonstrate that the proposed DLBI-based NLFA
model obtains higher representation accuracy several than state-of-the-art
models do, as well as highly-competitive computational efficiency.