Spectral Clustering

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2 papers in the last four weeks, against 2 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
Randomized SVD Approximations for Spectral Co-Clustering of Word-Document Matrices
Sep 16, 2026Fateme Mazdarani, Carlos ToxtliClustering Randomized Sketching
School of Computing Clemson University Clemson, SC, USA
SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging
Sep 7, 2026Yirui Zhang, Yixuan Tang, Yandong Sun +2Spectral Clustering
School of Computing, National University of Singapore
Spectral graph clustering with inhomogeneous latent geometry
Aug 11, 2026Konstantin Avrachenkov, Lucas S. Sibemberg, Alexander Van WerdeSpectral Clustering Graph Clustering
Inria Sophia Antipolis, France · Federal University of Rio Grande do Sul, Brazil · University of Münster, Germany
Spectral Embeddings of Degree- $α$ Laplacians in Random Dot Product Graphs
Aug 11, 2026John Park, Ning HaoClustering Spectral Clustering
University of Hong Kong · University of Arizona
Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
Aug 9, 2026Zeqin Lin, Guangming Pan, Zhixiang Zhang +1Clustering Unsupervised Clustering
1Nanyang Technological University · University of Macau · 3Dalian University of Technology
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniGraph Neural Networks Graph Pooling
University of Pisa, Dept. of Computer Science, 56127 Pisa PI, Italy · National Research Council, 56124 Pisa PI, Italy
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
Aug 4, 2026Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore TraniGPU Acceleration Clustering
University of Pisa, Dept. of Computer Science, 56127 Pisa PI, Italy · National Research Council, 56124 Pisa PI, Italy
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
Jul 3, 2026Arghya Pratihar, Shinjon Chakraborty, Swagatam DasCausal Effect Estimation Causal Structure Learning
1*Electronics and Communication Sciences Unit, Indian Statistical Institute, India. · 2Indian Statistical Institute, India.
Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering
May 21, 2026Zeqiang Xian, Caihui Liu, Yong Zhang +1Graph Structure Learning Clustering
Department of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, Jiangxi, China · Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, Jiangxi, China
Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations
May 4, 2026Cameron Berg, Susan L. Schneider, Mark M. BaileyAI Agent Monitoring Spectral Clustering
Reciprocal Research · New York, NY, USA · Center for the Future of AI, Mind, and Society +5
Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model
Apr 21, 2026Manuel Fernandez, Ludovic Stephan, Yizhe ZhuSpectral Clustering Stochastic Block Model
Department of Mathematics, University of Southern California · Univ Rennes, Ensai, CNRS, CREST-UMR 9194, F-35000 Rennes, France

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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