An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors

• URL: http://arxiv.org/abs/2208.06603v1
• Date: Sat, 13 Aug 2022 09:05:05 GMT
• Title: An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors
• Authors: Yuanyi Liu, Jia Chen and Di Wu
• Abstract summary: We propose a sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.
• Score: 10.129747457073389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A2BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.

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