arXiv Machine Learning August 11, 2026 By Vicente Conde Mendes, Lorenzo Bardone, C\'edric Koller, Jorge Medina Moreira, Vittorio Erba, Emanuele Troiani, Lenka Zdeborov\'a

A solvable high-dimensional model where nonlinear autoencoders learn structure invisible to PCA while test loss misaligns with generalization

Read the original on arXiv Machine Learning →

arXiv:2602. 10680v2 Announce Type: replace-cross Abstract: Many real-world datasets contain hidden structure that cannot be detected by simple linear correlations between input features.

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arXiv Machine Learning

Aug 10

PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder

arXiv:2601. 19179v2 Announce Type: replace Abstract: Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA).

By Qipeng Zhan, Zhuoping Zhou, Zexuan Wang, Li Shen

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arXiv Machine Learning

Jun 19

Adversarial Dependence Minimization

arXiv:2502. 03227v2 Announce Type: replace Abstract: Minimally redundant representations are typically learned by minimizing feature covariance.

By Pierre-Fran\c{c}ois De Plaen, Tinne Tuytelaars, Marc Proesmans, Luc Van Gool

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arXiv AI

Jun 30

Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation

arXiv:2606. 28854v1 Announce Type: cross Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a single-hidden-layer generative neural network.

By Carel F. W. Peeters

arXiv Machine Learning

Jun 2

Discovering Nonlinear Static Relationships in Unlabeled Dataset using Autoencoder with Ordered Variance

arXiv:2402. 14031v2 Announce Type: replace-cross Abstract: This paper presents an autoencoder with ordered variance (AEO), in which the conventional reconstruction loss is augmented by a variance-based regularization term that promotes an ordered structure within the latent space.

By Midhun T. Augustine, Parag Patil, Mani Bhushan, Sharad Bhartiya

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arXiv Machine Learning

Jul 8

Orthogonal Dendritic Intrinsic Networks: An Architecture for Significance-Ordered, Orthogonal Latent Spaces

arXiv:2607. 05653v1 Announce Type: new Abstract: Principal Component Analysis or PCA-like properties (orthogonality, variance ranking) are seldom realized in deep autoencoder architectures.

By Jeanie Schreiber, Tyrus Berry, Zeeshan Ahmed

arXiv Machine Learning

Jun 3

A Robust Optimization Approach to Sparse Principal Component Analysis

arXiv:2606. 03553v1 Announce Type: cross Abstract: While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dimensional data.

By David V\"avinggren, Francis Bach, Andr\'e M. H. Teixeira, Dave Zachariah, Ant\^onio H. Ribeiro

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