A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated update ...
Abstract—A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated.
Convergence Analysis of an SLF-NMU Algorithm for Non-negative Latent Factor Analysis on a High-Dimensional and Sparse Matrix ... model for faster convergence.
A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, ...
This work theoretically proves that with an appropriately chosen momentum coefficient, SLF-NM2U enables the fast convergence of an FNLF model in both ...
Apr 20, 2020 · This study proposes a generalized and fast-converging non-negative latent factor (GFNLF) model. Its main idea is two-fold.
Apr 20, 2020 · Its main idea is two-fold: a) adopting α-β-divergence for its objective function, thereby enhancing its representation ability for HiDS data; b) ...
Abstract—Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with ...
... A multilayered and randomized latent factor model was adopted to reduce the time complexity and enhance data representation for better understanding. In the ...
May 5, 2022 · This paper proposes a Proportional-Integral-incorporated Non-negative Latent Factor (PI-NLF) model with two-fold ideas.