arXiv Machine Learning

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).

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 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

diffusion

arXiv Machine Learning

Aug 11

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

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.

By Vicente Conde Mendes, Lorenzo Bardone, C\'edric Koller, Jorge Medina Moreira, Vittorio Erba, Emanuele Troiani, Lenka Zdeborov\'a

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

diffusion efficiency safety

arXiv Machine Learning

Jun 5

Anchor PCA

arXiv:2606. 06233v1 Announce Type: cross Abstract: Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques.

By Benedikt Seiter, Anya Fries, Julius von K\"ugelgen, Jonas Peters

rag

arXiv Machine Learning

Jun 30

Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning

arXiv:2605. 09160v2 Announce Type: replace Abstract: Learned representations are often invariant to rotational transformations, leaving individual dimensions non-identifiable and interchangeable.

By Arghamitra Talukder, Philippe Chlenski, Itsik Pe'er

arXiv Machine Learning

Jun 2

MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment

arXiv:2605. 29987v2 Announce Type: replace Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse.

By Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham

rag efficiency safety

arXiv AI

Jul 24

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.

By L\'ea Billet (LAAS, INSA Toulouse, ANITI), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS), Alexandre Gaffet

diffusion benchmarks

arXiv Machine Learning

Jul 15

Graph Regularized PCA

arXiv:2601. 10199v2 Announce Type: replace Abstract: Multivariate data often exhibit complex dependencies that violate the assumption of isotropic residual noise.

By Antonio Briola, Marwin Schmidt, Fabio Caccioli, Carlos Ros Perez, James Singleton, Christian Michler, Tomaso Aste

efficiency

arXiv Machine Learning

Jul 20

Manifold Dimension Estimation via Local Graph Structure

arXiv:2510. 15141v5 Announce Type: replace-cross Abstract: Most existing manifold dimension estimators rely on the assumption that the underlying manifold is locally flat within the neighborhoods under consideration.

By Zelong Bi, Pierre Lafaye de Micheaux

rag benchmarks

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

computer-vision safety

arXiv Machine Learning

Jun 4

Geometry-Preserving Encoder/Decoder in Latent Generative Models

arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.

By Wonjun Lee, Riley C. W. O'Neill, Dongmian Zou, Jeff Calder, Gilad Lerman

diffusion benchmarks

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