mlsanityData to 7 Oct 2026

An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning

arXiv 1611.08331 ↗First posted 25 Nov 2016cs.LG stat.MLcited by 29

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Abstract (arXiv)

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2016.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 1; 2024: 6; 2025: 8; 2026: 2.

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  1. 2024Dynamic and Adaptive Feature Generation with LLMcited by 27
  2. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
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  6. 2022A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Modelscited by 8
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  8. 2022Empirical Evaluation and Theoretical Analysis for Representation Learning: A Surveycited by 5
  9. 2021Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectorscited by 4
  10. 2024Machine Learning Analysis of Anomalous Diffusioncited by 3
  11. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  12. 2021Do Neural Network Weights account for Classes Centers?cited by 2
17 more
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  4. 2022Training Process of Unsupervised Learning Architecture for Gravity Spy Datasetcited by 1
  5. 2022Learning an Ensemble of Deep Fingerprint Representationscited by 1
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  9. 2022Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Spacecited by 0
  10. 2024TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reductioncited by 0
  11. 2024Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencodercited by 0
  12. 2025An Optimization Algorithm for Multimodal Data Alignmentcited by 0
  13. 2025Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Expertscited by 0
  14. 2025Innovative tokenisation of structured data for LLM trainingcited by 0
  15. 2025Learning Representations of Satellite Images with Evaluations on Synoptic Weather Eventscited by 0
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  17. 2026Computational Methods and Challenges in Cell-Free DNA Analysis for Multi-Cancer Early Detectioncited by 0