Scalable deep subspace clustering method improves image grouping accuracy
LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Clustering large groups of images by their underlying features can be slow and demand a lot of memory. The authors introduce LoomSC, a new method that breaks down complex calculations into smaller parts, making the process faster and more efficient. They tested their approach on several image datasets, showing it can handle very large collections accurately while using less computing power.
What this means in practice
- •For image processing teams: Cluster large image datasets efficiently for improved image organization and retrieval with reduced memory and time costs.
- •For big data engineers: Process millions of high-dimensional samples in scalable clustering pipelines while preserving accuracy for pattern discovery.
Authors
Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa, Riadh Ksantini, Shakeeb Murtaza, Tehseen Zia
Abstract
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.