mlsanityData to 7 Oct 2026

Local Contrastive Feature learning for Tabular Data

arXiv 2211.10549 ↗First posted 19 Nov 2022cs.LG cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 19 Nov 2022.

Builds on

17 of 25 references.

References · 17

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  5. 2021Revisiting Deep Learning Models for Tabular Datacited by 725
  6. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
  7. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  8. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  9. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  10. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 310
  11. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  12. 2021Well-tuned Simple Nets Excel on Tabular Datasetscited by 151
5 more
  1. 2021SCARF: Self-Supervised Contrastive Learning using Random Feature Corruptioncited by 151
  2. 2021SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learningcited by 101
  3. 2021Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domaincited by 20
  4. 2021Contrastive Learning of User Behavior Sequence for Context-Aware Document Rankingcited by 18
  5. 2022Data-Efficient and Interpretable Tabular Anomaly Detectioncited by 15

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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2023ReConTab: Regularized Contrastive Representation Learning for Tabular Datacited by 24
  2. 2024Unveiling the Power of Sparse Neural Networks for Feature Selectioncited by 3
  3. 2024VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purificationcited by 3
  4. 2024BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learningcited by 0