English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A high-dimensional and incomplete (HDI) matrix can describe the complexinteractions among numerous nodes in various big data-related applications. Astochastic gradient descent (SGD)-based latent factor analysis (LFA) model isremarkably effective in extracting valuable information from an HDI matrix.However, such a model commonly encounters the problem of slow convergencebecause a standard SGD algorithm learns a latent factor relying on thestochastic gradient of current instance error only without considering pastupdate information. To address this critical issue, this paper innovativelyproposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1)rebuilding the instance learning error by considering the past updateinformation in an efficient way following the principle of PID, and 2)implementing hyper-parameters and gain parameters adaptation following thefuzzy rules. With it, an FPS-incorporated LFA model is further achieved forfast processing an HDI matrix. Empirical studies on six HDI datasetsdemonstrate that the proposed FPS-incorporated LFA model significantlyoutperforms the state-of-the-art LFA models in terms of computationalefficiency for predicting the missing data of an HDI matrix with competitiveaccuracy.

Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm