Atomic Recovery Property for Multi-view Subspace-Preserving Recovery
Authors: Yulong Wang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the multi-view subspace-preserving recovery theory, which plays a critical role for multi-view subspace clustering and classification. We first generalize the atomic norm to multi-view data and define the Multi-view Atomic Norm (MAN). Based on MAN, we derived a necessary and sufficient condition dubbed ARP (Atomic Recovery Property) for the JSR model to produce MSPR. To the best of our knowledge, ARP is the first necessary and sufficient theoretical condition for multi-view subspace-preserving recovery. The results reveal important geometric sights and provide theoretical justification for the success of multi-view subspace clustering and classification. |
| Researcher Affiliation | Academia | 1 College of Informatics, Huazhong Agricultural University, China 2 Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, China 3 Key Laboratory of Smart Farming Technology for Agricultural Animals, Ministry of Agriculture and Rural Affairs, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical training on datasets. Therefore, no information about public datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation on datasets. Therefore, no information about dataset splits for validation is provided. |
| Hardware Specification | No | This is a theoretical paper that does not describe empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not describe empirical experiments requiring specific software dependencies with version numbers. Therefore, no software dependencies are mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |