Deep Clustering

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2 papers in the last four weeks, with none 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
DeepTopoClustering: Unsupervised Derivation of Surface Process Taxonomy from 4D Point Clouds for Topographic Monitoring
Oct 7, 2026Jiapan Wang, Daan Hulskemper, Mathilde Letard +2Deep Clustering 4D Scene Understanding
Remote Sensing Applications, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany · Department of Geoscience & Remote Sensing, Delft University of Technology, Delft, The Netherlands
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Oct 5, 2026Haemin Park, Diego Klabjan, Martin W. Braun +2Clustering Deep Clustering
Northwestern University · Intel Corporation
VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training
Sep 29, 2026Yuanwei Hu, Bo Peng, Yuheng Jia +3Clustering Deep Clustering
University College London · The University of Queensland · Southeast University +2
Selective Inference for Deep Clustering in Latent Spaces
Sep 23, 2026Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1Selective Inference Clustering
Nagoya University · RIKEN
Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
Jul 31, 2026Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1Class-Imbalanced Learning Clustering
Department of Computer Science Tennessee State University Nashville, TN, USA · Enosis Solutions Dhaka, Bangladesh · Department of Computer Science North Carolina A&T State University Greensboro, NC, USA
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Jul 8, 2026Canlin Zhang, Xiuwen LiuUnsupervised Learning Self-Supervised Learning
Independent Researcher · Department of Computer Science Florida State University
Cohort Organized Learning: Clustering Through Agreement
Jun 19, 2026Finn Henry O'Shea, Maria Elena MonzaniUnsupervised Learning Clustering
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025-7015, USA · Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305-4085 USA
UniFair: A unified fair clustering approach based on separation and compactness
Jun 3, 2026Antonia Karra, Vasiliki Papanikou, Georgios Vardakas +2Clustering Algorithmic Fairness
University of Ioannina, Greece · Archimedes/Athena Research Center, Greece
CLUBench: A Clustering Benchmark
May 28, 2026Feng Xiao, Dazhi Fu, Chris Ding +1Benchmark Design Clustering
The Chinese University of Hong Kong (Shenzhen)

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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