论文标题
一种用于完善潜在因素的亚当调整算法
An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors
论文作者
论文摘要
在高维和不完整的矩阵中提取潜在信息是一个重要且具有挑战性的问题。潜在因子分析（LFA）模型可以很好地处理高维矩阵分析。最近，已经提出了粒子群优化（PSO）组合的LFA模型，以高效率调节超参数。但是，PSO的掺入会导致过早问题。为了解决这个问题，我们提出了一个顺序的Adam-unjusting-Antennae BAS（A2BAS）优化算法，该算法完善了由PSO成立的LFA模型获得的潜在因素。 A2BAS算法由两个子算法组成。首先，我们设计了一种改进的BAS算法，该算法可调节甲虫的天线并使用Adam进行尺寸。其次，我们实施了改进的BAS算法，以顺序优化所有行和列潜在因子。通过两个实际的高维矩阵的实验结果，我们证明了我们的算法可以有效地解决过早的收敛问题。
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.
