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High-Dimensional and Incomplete (HDI) data is commonly encountered in bigdata-related applications like social network services systems, which areconcerning the limited interactions among numerous nodes. Knowledge acquisitionfrom HDI data is a vital issue in the domain of data science due to theirembedded rich patterns like node behaviors, where the fundamental task is toperform 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 thetraining overshooting and fluctuation, as well as preventing the model frompremature convergence. However, existing LBI schemes are all statistic oneswhere the linear biases are fixed, which significantly restricts thescalability of the resultant NLFA model and results in loss of representationlearning ability to HDI data. Motivated by the above discoveries, this paperinnovatively presents the dynamic linear bias incorporation (DLBI) scheme. Itfirstly extends the linear bias vectors into matrices, and then builds a binaryweight matrix to switch the active/inactive states of the linear biases. Theweight matrix's each entry switches between the binary states dynamicallycorresponding to the linear bias value variation, thereby establishing thedynamic linear biases for an NLFA model. Empirical studies on three HDIdatasets from real applications demonstrate that the proposed DLBI-based NLFAmodel obtains higher representation accuracy several than state-of-the-artmodels do, as well as highly-competitive computational efficiency.

A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis