mlsanityData to 14 Oct 2026

An Additive Autoencoder for Dimension Estimation

arXiv 2210.06773 ↗First posted 13 Oct 2022cs.LG cs.AIcited by 3

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Abstract (arXiv)

An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsic dimension of a dataset with a low autoencoding error. This observation leads to an investigation in which shallow and deep network structures, and how they are trained, are compared. We conclude that the deeper network structures obtain lower autoencoding errors during the identification of the intrinsic dimension. However, the detected dimension does not change compared to a shallow network.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 79 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

14 of 79 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  3. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  4. 2018Deep learning for time series classification: a reviewcited by 576
  5. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  6. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
  7. 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 250
  8. 2018A Comprehensive Analysis of Deep Regressioncited by 48
  9. 2019N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embeddingcited by 30
  10. 2017Supervised Dimensionality Reduction for Big Datacited by 13
  11. 2022Accurate Node Feature Estimation with Structured Variational Graph Autoencodercited by 12
  12. 2020Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problemscited by 6
2 more
  1. 2018Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoderscited by 4
  2. 2019Dimension Estimation Using Autoencoderscited by 3

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methodscited by 3
  2. 2024Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototypingcited by 0
  3. 2025SOSAE: Self-Organizing Sparse AutoEncodercited by 0