Structured Matrix Recovery via the Generalized Dantzig Selector
Authors: Sheng Chen, Arindam Banerjee
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we present non-asymptotic analysis for estimation of generally structured matrices via the generalized Dantzig selector based on sub-Gaussian measurements. We show that the estimation error can always be succinctly expressed in terms of a few geometric measures such as Gaussian widths of suitable sets associated with the structure of the underlying true matrix. Further, we derive general bounds on these geometric measures for structures characterized by unitarily invariant norms, a large family covering most matrix norms of practical interest. Examples are provided to illustrate the utility of our theoretical development. |
| Researcher Affiliation | Academia | Sheng Chen Arindam Banerjee Dept. of Computer Science & Engineering University of Minnesota, Twin Cities {shengc,banerjee}@cs.umn.edu |
| Pseudocode | No | The paper contains mathematical derivations and theorems but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not describe training models on publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments, therefore no specific experimental setup details or hyperparameters are provided. |