large scale canonical correlation analysis with iterative least squares
Authors: Yichao Lu, Dean P. Foster
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments also show that L-CCA outperform other fast CCA approximation schemes on two real datasets. |
| Researcher Affiliation | Collaboration | Yichao Lu University of Pennsylvania yichaolu@wharton.upenn.edu Dean P. Foster Yahoo Labs, NYC dean@foster.net |
| Pseudocode | Yes | Algorithm 1 CCA via Iterative LS, Algorithm 2 LING, and Algorithm 3 L-CCA are presented in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of source code for the described methodology. |
| Open Datasets | Yes | The dataset used is the full Wall Street Journal Part of Penn Tree Bank which consists of 1.17 million tokens and a vocabulary size of 43k [18]. |
| Dataset Splits | No | The paper describes the datasets used and performance comparisons but does not explicitly provide specific train/validation/test dataset splits or cross-validation details needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments, only mentioning 'CPU time'. |
| Software Dependencies | No | The paper mentions 'matlab built-in CCA function' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In the experiments we fix t1 = 5 and vary t2. In both experiments we fix kpc = 100... For RPCCA L-CCA G-CCA we try three different parameter set ups shown in table 1 and the 20 correlations are shown in figure 1. |