Geometric Conditions for Subspace-Sparse Recovery
Authors: Chong You, Rene Vidal
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study conditions under which existing pursuit methods recover a subspace-sparse representation. Such conditions reveal important geometric insights and have implications for the theory of classical sparse recovery as well as subspace clustering. The main goal of this paper is thus to study the conditions on Φ, Ψ under which these two algorithms give subspace-sparse solutions. Furthermore, we obtain new theoretical conditions for classical sparse recovery as well as subspace clustering. |
| Researcher Affiliation | Academia | Chong You CYOU@CIS.JHU.EDU Ren e Vidal RVIDAL@CIS.JHU.EDU Center for Imaging Science, Johns Hopkins University, Baltimore, MD, 21218, USA |
| Pseudocode | No | The paper describes the OMP and BP algorithms textually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and focuses on deriving conditions for sparse recovery. It does not describe experiments run on a specific dataset or provide access information for any dataset used for training. |
| Dataset Splits | No | This paper is theoretical and does not describe an experimental setup involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical conditions and proofs. It does not provide details about an experimental setup, hyperparameters, or training settings. |