6 papers
Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model
Jialiang Wang, Weiling Li, Yurong Zhong +1
Interactions among large number of entities is naturally high-dimensional and incomplete (HDI) in many big data related tasks. Behavioral characteristics of users are hidden in the…
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis
Yurong Zhong, Zhe Xie, Weiling Li +1
High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning the limited interacti…
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
Yurong Zhong, Zhe Xie, Weiling Li +1
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e.g., a protein interacti…
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
Yurong Zhong, Zhe Xie, Weiling Li +1
An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed…
A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization
Jialiang Wang, Yurong Zhong, Weiling Li
Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient me…
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks
Zhe Xie, Weiling Li, Yurong Zhong
Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI)…