2022/08/12 by Jialiang Wang, Wang, Jialiang, Yurong Zhong +3
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2208.06125
openalex publication_date 2022/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 method to utilizing second-order information of an LF model's objective function and it has been utilized to optimize second-order LF (SLF) model. However, the low-rank representation ability of a SLF model heavily relies on its multiple hyperparameters. Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model. To address this issue, a practical SLF (PSLF) model is proposed in this work. It realizes hyperparameter self-adaptation with a distributed particle swarm optimizer (DPSO), which is gradient-free and parallelized. Experiments on real HiDS data sets indicate that PSLF model has a competitive advantage over state-of-the-art models in data representation ability.