Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Closed Form Solution to Multi-View Low-Rank Regression
Authors: Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multiview low-rank structure is very helpful. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering University of Texas at Arlington, TX, USA EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-view low-rank regression |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | These datasets include image datasets MSRC (Lee and Grauman 2009) and Caltech (Fei-Fei, Fergus, and Perona 2007), website dataset Cornell (Craven et al. 2000) and scientific publication dataset Cora (Mc Callum et al. 1999). Cornell and Cora are downloaded from (Grimal 2014). |
| Dataset Splits | No | The paper discusses ranks 's = 1, ..., c 1' and the use of bias but does not specify clear train/validation/test dataset splits (e.g., percentages or sample counts) for reproduction. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Regularization weight parameter λν... we choose λν as the average of all eigenvalues of XνXT ν , which is λν = 1. In the following experiments, the default setting of every experiment is using λν = 1. In the following experiments, the default setting of all experiments is using bias. |