Online Robust Low Rank Matrix Recovery
Authors: Xiaojie Guo
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data are conducted to demonstrate the efficacy of our method and show its superior performance over the state-of-the-arts. 3 Experimental Verification In this section, we conduct experiments on synthetic data to reveal the convergence speed and the robustness to outliers of the proposed ORLRMR, and on real data for showing the advantages of our method compared against alternative approaches. |
| Researcher Affiliation | Academia | Xiaojie Guo State Key Laboratory of Information Security Institute of Information Engineering, Chinese Academy of Sciences xj.max.guo@gmail.com |
| Pseudocode | Yes | Algorithm 1: The Basis Update; Algorithm 2: Online Low Rank Matrix Completion; Algorithm 3: Online Robust LRMR |
| Open Source Code | No | No explicit statement or link providing access to the authors' own open-source code for the described methodology was found. |
| Open Datasets | No | The paper mentions 'The Star dataset consists of 9 real world surveillance videos' but does not provide a formal citation (with author names and year), URL, DOI, or specific repository name for public access to this dataset. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce data partitioning for training, validation, and testing. |
| Hardware Specification | Yes | The experiments are carried out on a Mac Book Pro running OS X 64bit operating system with Intel Core i.7 2.8GHz CPU and 16GB RAM. |
| Software Dependencies | No | The paper mentions 'Our code is implemented in Matlab' and discusses 'C++', but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | For the experiments shown in this paper, we uniformly set λ to 2. As the rank of the background of surveillance videos is typically small, we empirically set it to 5 for all the 9 sequences. |