🤖 AI Summary
This work addresses the limitations of existing latent factor models in handling high-dimensional incomplete (HDI) data, which often yield biased and insufficient representations due to their sole reliance on gradient descent optimization. To overcome this issue, the authors propose a novel heterogeneous ensemble approach that uniquely integrates differential evolution and gradient descent to construct two complementary latent factor models. An adaptive weighting mechanism is further introduced to dynamically fuse the strengths of both models during training. This strategy effectively mitigates representation bias and consistently outperforms several state-of-the-art latent factor models across three HDI datasets, demonstrating its superior effectiveness and robustness.
📝 Abstract
High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios. Latent factor models serve as a common representation learning approach, capable of uncovering informative latent factors from such data. Nevertheless, most existing latent factor models rely solely on gradient descent for optimization, which may lead to insufficient and biased representations, particularly when dealing with heterogeneous HDI data. Thus, this study proposes an Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization (ELFM-DEGDO) with two-fold designed: 1) two diverse latent factor models are independently modeled via differential evolution and gradient descent optimization, respectively, and 2) the two diverse latent factor models are combined via a customized self-adaptive weighting mechanism to effectively fuse their strengths. By leveraging the complementary advantages of both optimization paradigms, ELFM-DEGDO is able to produce more comprehensive and less biased representations for HDI data. Three HDI datasets are tested to show that ELFM-DEGDO consistently performs better than related several latent factor models.