Local Contrastive Feature learning for Tabular Data

TL;DR

This paper introduces LoCL, a local contrastive learning framework for tabular data that leverages feature correlations and convolutional learning to improve feature extraction without labels.

Contribution

It proposes a novel local contrastive learning approach for tabular data using feature correlation-based feature subsets and convolutional networks.

Findings

Outperforms state-of-the-art methods on public tabular datasets

Effectively captures local feature patterns in tabular data

Demonstrates robustness across different datasets

Abstract

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.