AI 中文总结
针对实际机器学习应用中不完整观测问题，提出FILLER特征插补方法，在生成模型潜在空间搜索填充缺失值，经数学证明、多数据集评估及与现有方法对比等，验证了该方法的有效性。
AI 中文摘要
在实际机器学习应用中，不完整观测带来根本挑战，现有模型在平衡可扩展性和结构一致性方面仍有困难。本研究提出FILLER特征插补方法，在生成模型产生的二维潜在空间中搜索，用合适条目填充缺失值。以G-NeuroDAVIS作为生成模型，给出迭代搜索收敛的数学证明。在多个图像数据集上评估，与现有方法对比，并进行Wilcoxon符号秩检验及下游分析验证效果。
英文摘要
In real-world machine learning applications, incomplete observations create a fundamental challenge. Researchers have come up with several ideas to address this crucial problem. However, current models still face challenges in balancing scalability and structural consistency. This study proposes a feature imputation method, called FILLER, that deliberately searches the two-dimensional latent space produced by a generative model and fills the missing values with appropriate entries. The generative model is trained on fully observed data to generate samples from the latent space, and FILLER uses this trained model to impute the values missing in the corrupted test samples. In this study, G-NeuroDAVIS serves the purpose of the generative model. This work also presents a mathematical proof on the convergence of the iterative search. Finally, FILLER has been evaluated on several image datasets under random and structured missingness patterns with varying levels of imputation complexities. In order to justify the efficacy of FILLER, it has been compared against existing state-of-the-art solution strategies in terms of RMSE, PSNR, and SSIM. In addition, Wilcoxon signed-rank test has been carried out to validate statistical significance. Moreover, downstream analyses (classification and clustering) have also established the quality of imputation in terms of standard metrics.
