Orthogonal Representation Learning

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1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Oct 7, 2026Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa +3Clustering Spectral Clustering
École de technologie supérieure (ÉTS), Montréal, QC, Canada · University of Bahrain (UOB), Kingdom of Bahrain · Université du Québec à Montréal (UQAM), Montréal, QC, Canada +1
Learning Orthogonal Multi-Index Models Beyond Small Initialization: Incremental Learning, Competitive Dynamics and Symmetry
Sep 11, 2026Mo Zhou, Weihang Xu, Simon S. Du +1Shallow Neural Networks Orthogonal Representation Learning
University of Washington · Amazon, Inc.
Orthogonal JEPA: Factorized Predictive States for Latent World Models
Aug 20, 2026Taoyong Cui, Pheng Ann Heng, Wanli OuyangJoint-Embedding Predictive Architecture Orthogonal Representation Learning
The Chinese University of Hong Kong (CUHK)
Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration
Aug 13, 2026Sabin Roman, Ljupco Todorovski, Saso DzeroskiSparse Identification of Nonlinear Dynamics Nonlinear System Identification
Department of Knowledge Technologies, Jožef Stefan Institute · Faculty of Mathematics and Physics, University of Ljubljana
Exploring Oversmoothing with Householder Matrices
Aug 12, 2026Bhaskar KarolGraph Neural Networks Orthogonal Representation Learning
OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning
Jul 31, 2026Amr M. Zaki, Farhoud Jafari Kaleibar, Honggeun Ji +2Edge Inference Orthogonal Representation Learning
School of Engineering, York University, Toronto, ON, Canada.
Projection Pursuit CPCANet for Domain Generalization
Jul 24, 2026Yu-Hsi Chen, Abd-Krim SeghouanePrincipal Component Analysis Domain Generalization
The University of Melbourne
Gradient-Based Latent Decomposition Reveals Mechanisms of Feature Degradation in Weakly Supervised Mammography
Jul 24, 2026Vinceline Bertrand, Ionut CardeiVariational Autoencoders Hierarchical Representation Learning
Florida Atlantic University, Boca Raton, FL, USA
Escaping the Procrustean Bed: Groupwise Orthogonal Connectors for Audio-Language Models
Jul 7, 2026Ho-Lam Chung, Ke-Han Lu, Yi-Cheng Lin +3Audio Representation Learning Representation Learning
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · ASUS AICS, Taipei, Taiwan
Orthogonal Dendritic Intrinsic Networks: An Architecture for Significance-Ordered, Orthogonal Latent Spaces
Jul 6, 2026Jeanie Schreiber, Tyrus Berry, Zeeshan AhmedRepresentation Learning Principal Component Analysis
Department of Mathematical Sciences George Mason University Fairfax, VA 22030, USA · Sensor Science Division, Physical Measurement Laboratory NIST Gaithersburg, MD 20899, USA
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Jun 19, 2026Mathieu Cyrille Simon, Pascal Frossard, Christophe De VleeschouwerUnsupervised Learning Disentangled Representation Learning
1UCLouvain, ICTEAM, Louvain-la-Neuve, Belgium · 2EPFL, LTS4 laboratory, Lausanne, Switzerland.
Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection
Jun 15, 2026Zhuodong Liu, Hugen Lv, Xiangyu Li +1Representation Learning Audio Deepfake Detection
Beijing Jiaotong University, China · Shanghai Jiao Tong University, China · ITMO University, Russia
OSCS-SupCon: Orthogonal Sigmoid-based Common and Style Supervised Contrastive Learning for Robust Feature Disentanglement
May 29, 2026Bin Wang, Fadi DornaikaContrastive Learning Disentangled Representation Learning
University B.of theWang1Basqueand Country · IKERBASQUE,2
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
May 25, 2026Aleksandar Todorov, Matthia SabatelliRepresentation Learning Reinforcement Learning
University of Groningen, Groningen, The Netherlands
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
May 18, 2026Yuanyun Zhang, Shi LiDisentangled Representation Learning Representation Learning
University of the Chinese Academy of Sciences · Columbia University
The E $Δ$ -MHC-Geo Transformer: Adaptive Geodesic Operations with Guaranteed Orthogonality
May 7, 2026Arash ShahmansooriTransformer Residual Learning
Independent Researcher
Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data
May 5, 2026He Lyu, Huolin Zeng, Junren Wang +7Disentangled Representation Learning Representation Learning
Department of Anesthesiology and West China Biomedical Big Data Center West China Hospital, Sichuan University, Chengdu, China · Med-X Center for Informatics, Sichuan University, Chengdu, China · Department of National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China +4
CCAR: Intrinsic Robustness as an Emergent Geometric Property
Apr 18, 2026Akash Samanta, Manish Pratap Singh, Debasis ChaudhuriDisentangled Representation Learning Representation Learning
Techno India University, Salt Lake, Kolkata, India · DRDO Young Scientist Laboratory - CT, Chennai, India
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
Feb 27, 2026Arnas Uselis, Andrea Dittadi, Seong Joon OhNeural Representation Geometry Visual Representation Learning
Tübingen AI Center, University of Tübingen · Helmholtz · Technical University of Munich
From Core to Detail: Unsupervised Disentanglement with Entropy-Ordered Flows
Feb 6, 2026Daniel Galperin, Ullrich KötheUnsupervised Learning Disentangled Representation Learning
Heidelberg University
Towards Isolated Interventions via Almost Orthogonal Features in Language Models
Feb 4, 2026Moritz Miller, Florent Draye, Bernhard SchölkopfDisentangled Representation Learning Representation Learning
Max Planck Institute for Intelligent Systems, Tübingen, Germany · ETH Zurich, Switzerland · Max Planck ETH Center for Learning Systems +1

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction

Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa, Riadh Ksantini+2

École de technologie supérieure (ÉTS), Montréal, QC, Canada · University of Bahrain (UOB), Kingdom of Bahrain · Université du Québec à Montréal (UQAM), Montréal, QC, Canada+1

Clustering Spectral Clustering Deep Clustering Image Clustering Orthogonal Representation Learning

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Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Optimization Model for Subspace Clustering (LoomSC), a framework that addresses both bottlenecks through projector factorization and exact spectral reduction. Motivated by the spectral structure of least-squares regression, LoomSC jointly learns latent features and a projector self-representation through two thin factors. Alternating Procrustes and least-squares updates preserve the sample factor's orthogonality while keeping the coefficient matrix implicit. We construct a nonnegative quadratic affinity that preserves the projector's support. An exact feature map then reduces its normalized spectral problem to an eigenproblem whose dimension depends only on the factor width. Neither the full affinity nor the sample Laplacian needs to be formed. Our analysis quantifies the projector approximation and identifies conditions for subspace preservation and within-subspace connectivity. For fixed dimensions and iteration budgets, the complete pipeline has linear time and memory complexity in the number of samples. Across five image-clustering benchmarks, LoomSC ranks first or second in all 15 dataset-metric comparisons against 9 state-of-the-art baselines. Its mean accuracy exceeds the highest baseline mean by 6.66 percentage points. Synthetic experiments scale to 500,000 samples while maintaining at least 99.8% accuracy.

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