| Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces | 1 | 0.34 | 2022 |
| Incremental Learning via Rate Reduction | 0 | 0.34 | 2021 |
| Learning a Self-Expressive Network for Subspace Clustering | 0 | 0.34 | 2021 |
| A Critique of Self-Expressive Deep Subspace Clustering | 0 | 0.34 | 2021 |
| A Nullspace Property For Subspace-Preserving Recovery | 0 | 0.34 | 2021 |
| Stochastic Sparse Subspace Clustering | 0 | 0.34 | 2020 |
| Deep Isometric Learning for Visual Recognition | 0 | 0.34 | 2020 |
| Rethinking Bias-Variance Trade-off for Generalization of Neural Networks | 0 | 0.34 | 2020 |
| Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization | 0 | 0.34 | 2020 |
| Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction | 0 | 0.34 | 2020 |
| Classifying and Comparing Approaches to Subspace Clustering with Missing Data | 0 | 0.34 | 2019 |
| Self-Supervised Convolutional Subspace Clustering Network | 7 | 0.40 | 2019 |
| A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis. | 1 | 0.36 | 2018 |
| On Geometric Analysis of Affine Sparse Subspace Clustering. | 5 | 0.39 | 2018 |
| Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework. | 39 | 0.81 | 2017 |
| A Divide-And-Conquer Framework For Large-Scale Subspace Clustering | 1 | 0.34 | 2016 |
| Scalable Sparse Subspace Clustering By Orthogonal Matching Pursuit | 27 | 0.67 | 2016 |
| Oracle Based Active Set Algorithm For Scalable Elastic Net Subspace Clustering | 41 | 0.89 | 2016 |
| Geometric Conditions for Subspace-Sparse Recovery | 10 | 0.47 | 2015 |
