论文标题
表格数据的本地对比功能学习
Local Contrastive Feature learning for Tabular Data
论文作者
论文摘要
对比性自我监督学习已成功地用于许多域，例如图像，文本，图形等，以学习功能而无需标签信息。在本文中，我们提出了一个新的本地对比功能学习（LIGL）框架，我们的主题是从表格数据中学习本地模式/功能。为了创建用于本地学习的利基市场，我们使用功能相关性来创建最大跨度树，并将树分解为特征子集中，彼此相邻分配强烈的相关特征。对特征的卷积学习用于学习潜在的特征空间，并由对比和重建损失调节。公共表格数据集的实验显示了所提出的方法与最先进的基线方法的有效性。
Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information. In this paper, we propose a new local contrastive feature learning (LoCL) framework, and our theme is to learn local patterns/features from tabular data. In order to create a niche for local learning, we use feature correlations to create a maximum-spanning tree, and break the tree into feature subsets, with strongly correlated features being assigned next to each other. Convolutional learning of the features is used to learn latent feature space, regulated by contrastive and reconstruction losses. Experiments on public tabular datasets show the effectiveness of the proposed method versus state-of-the-art baseline methods.
