Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label information to assist in this task, their simplistic utilization of labels lacks flexibility and may rely on strict assumptions. In this paper, we propose a new framework that effectively leverages supervision information to complete missing data in a manner conducive to classification. Specifically, this framework operates in two stages. Firstly, it leverages labels to supervise the optimization of similarity relationships among data, represented by the kernel matrix, with the goal of enhancing classification accuracy. To mitigate overfitting that may occur during this process, a perturbation variable is introduced to improve the robustness of the framework. Secondly, the learned kernel matrix serves as additional supervision information to guide data imputation through regression, utilizing the block coordinate descent method. The superiority of the proposed method is evaluated on four real-world data sets by comparing it with state-of-the-art imputation methods. Remarkably, our algorithm significantly outperforms other methods when the data is missing more than 60\% of the features

翻译：数据插补，即针对不完整数据集填充缺失特征元素的过程，在数据驱动的学习中扮演着关键角色。其核心理念在于，数据插补有助于提升学习性能，因此追求更好的分类结果可以指导数据插补过程。尽管已有研究尝试利用标签信息辅助该任务，但这些方法对标签的简单化利用缺乏灵活性，且可能依赖于严格假设。本文提出一种新框架，能够有效利用监督信息以促进分类的方式完成缺失数据填补。具体而言，该框架分为两个阶段：首先，利用标签监督数据间相似关系（以核矩阵表示）的优化过程，旨在提升分类精度；为缓解该过程中可能出现的过拟合问题，引入扰动变量增强框架鲁棒性。其次，将学习得到的核矩阵作为附加监督信息，通过块坐标下降法以回归方式指导数据插补。通过将所提方法与四种先进插补技术在真实数据集上进行对比评估，结果表明：当数据缺失超过60%特征时，本算法性能显著优于其他方法。