Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method

TL;DR

This paper introduces a supervised kernel-based framework for data imputation that enhances classification performance by leveraging label information and robust optimization, outperforming existing methods especially with high missing data rates.

Contribution

The paper proposes a novel two-stage supervised kernel-based method that uses label-guided similarity optimization and kernel-driven regression for data imputation, improving robustness and classification accuracy.

Findings

Significantly outperforms state-of-the-art imputation methods with over 60% missing data.

Utilizes label information to optimize similarity relationships in kernel space.

Introduces a perturbation variable to enhance robustness against overfitting.

Abstract

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