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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |