PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis. (arXiv:2205.02591v1 [cs.LG])

A high-dimensional and incomplete (HDI) matrix frequently appears in various
big-data-related applications, which demonstrates the inherently non-negative
interactions among numerous nodes. A non-negative latent factor (NLF) model
performs efficient representation learning to an HDI matrix, whose learning
process mostly relies on a single latent factor-dependent, non-negative and
multiplicative update (SLF-NMU) algorithm. However, an SLF-NMU algorithm
updates a latent factor based on the current update increment only without
appropriate considerations of past learning information, resulting in slow
convergence. Inspired by the …

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