Masked Subspace Clustering Methods
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Abstract (arXiv)
To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering. And then we introduce three special cases on subspace clustering with two different types of masks. At first, we reformulate the original subspace clustering as a Basic Masked Subspace Clustering (BMSC), which reformulate the diagonal constraints to a hard mask. Then, we provide a General Masked Subspace Clustering (GMSC) method to integrate different clustering via a soft mask. Furthermore, based on BCO and GMSC, we induce a learnable soft mask and design a Recursive Masked Subspace Clustering (RMSC) method that can alternately update the affinity matrix and the soft mask. Numerical experiments show that our models obtain significant improvement compared with the baselines on several commonly used datasets, such as MNIST, USPS, ORL, COIL20 and COIL100.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 11 May 2025.
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8 of 26 references.
References · 8
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2020Meta-Learning in Neural Networks: A Surveycited by 950
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2021Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyondcited by 153
- 2015Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuitcited by 50
- 2018Subspace Clustering by Block Diagonal Representationcited by 38
- 2019Self-Supervised Convolutional Subspace Clustering Networkcited by 32
- 2016Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Frameworkcited by 26
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