An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single ...
Convergence Analysis of an SLF-NMU Algorithm for Non-negative Latent Factor Analysis on a High-Dimensional and Sparse Matrix. October 2019. October 2019.
Convergence Analysis of an SLF-NMU Algorithm for Non-negative ...
dl.acm.org › doi › SMC.2019.8913926
An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single ...
Abstract—Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection.
HIGH-DIMENSIONAL AND SPARSE (HiDS) matrix is commonly adopted to describe incomplete interactions among concerning objects in big data-related applications, ...
A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU) learning algorithm is highly efficient in building a nonnegative LF ...
This study conducts rigorous convergence analysis for an SLF-NMU-based NLF model and proves that it converges to a stable equilibrium point with its SLf-NM ...
Empirical studies on six large sparse matrices from different recommendation service applications show that a GNALF model achieves very high convergence rate ...
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative ...
The convergence analysis in this paper innovatively shows that an SLF-NMU learning algorithm can enable an NLF model to converge on an arbitrary matrix in spite ...