Abstract: Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with ...

To build a fast NLF model, we firstly propose a generalized momentum method compatible with SLF-NMU. With it, we propose the single latent factor-dependent, non ...

Abstract—Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with ...

The single latent factor-dependent, non-negative, multiplicative and momentum-integrated update (SLF-NM2U) algorithm for accelerating the building process ...

Accelerated Non-negative Latent Factor Analysis on High-Dimensional and Sparse Matrices via Generalized Momentum Method.

Bibliographic details on Accelerated Non-negative Latent Factor Analysis on High-Dimensional and Sparse Matrices via Generalized Momentum Method.

Abstract—Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with ...

Luo et al. [70] proposed a method called the fast non-negative Latent Factor model, which is effective for working with high-dimensional data, a popular topic ...

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, ...

Abstract—Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional ...