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..
Towards Practical Alternating Least-Squares for CCA
Authors: Zhiqiang Xu, Ping Li
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several datasets empirically demonstrate the superiority of the proposed algorithms to several recent variants of CCA solvers. |
| Researcher Affiliation | Industry | Zhiqiang Xu and Ping Li Cognitive Computing Lab Baidu Research No.10 Xibeiwang East Road, Beijing, 10085, China 10900 NE 8th St, Bellevue, WA 98004, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 TALS-CCA, Algorithm 2 FALS-CCA, Algorithm 3 AALS-CCA |
| Open Source Code | No | The paper states 'All the algorithms were implemented in MATLAB,' but does not provide any explicit statement about making the source code open or available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Three real-world datasets are used: Mediamill [18], JW11 [17], and MNIST [14]. |
| Dataset Splits | No | The paper mentions using three real-world datasets (Mediamill, JW11, MNIST) and describes solver iterations, but it does not specify any explicit train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | Yes | All the algorithms were implemented in MATLAB, and run on a laptop with 8 GB memory. |
| Software Dependencies | No | The paper states 'All the algorithms were implemented in MATLAB,' but does not provide specific version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | Regularization parameters are fixed to rx = ry = 0.1. Stochastic variance reduced gradient (SVRG) is the least-squares solver we use for each algorithm. Throughout the experiments the solver runs 2 epochs with each running n iterations with constant step-sizes αΦ = 1 maxi xi 2 2 for Φt and αΨ = 1 maxi yi 2 2 for Ψt, where xi is the i-th column of X. |