Stochastic Approximation for Canonical Correlation Analysis
Authors: Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results for our proposed methods, in particular we compare capped-MSG which is the practical variant of Algorithm 1 with capping as defined in equation (10), and MEG (Algorithm 2 in the Appendix), on a real dataset, Mediamill [19], consisting of paired observations of videos and corresponding commentary. We compare our algorithms against CCALin of [8], ALS CCA of [24]2, and SAA, which is denoted by batch in Figure 1. |
| Researcher Affiliation | Academia | Raman Arora Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 arora@cs.jhu.edu Teodor V. Marinov Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 tmarino2@jhu.edu Poorya Mianjy Dept. of Computer Science Johns Hopkins University Baltimore, MD 21204 mianjy@jhu.edu Nathan Srebro TTI-Chicago Chicago, Illinois 60637 nati@ttic.edu |
| Pseudocode | Yes | Algorithm 1 Matrix Stochastic Gradient for CCA (MSG-CCA) Input: Training data {(xt, yt)}T t=1, step size , auxiliary training data {(x0 i=1 Output: M |
| Open Source Code | Yes | We make our implementation of the proposed algorithms and existing competing techniques available online1. 1https://www.dropbox.com/sh/dkz4zgkevfyzif3/AABK9JlUvIUYtHvLPCBXLlpha?dl=0 |
| Open Datasets | Yes | We provide experimental results for our proposed methods, in particular we compare capped-MSG which is the practical variant of Algorithm 1 with capping as defined in equation (10), and MEG (Algorithm 2 in the Appendix), on a real dataset, Mediamill [19], consisting of paired observations of videos and corresponding commentary. |
| Dataset Splits | No | The paper mentions 'Training data' for Algorithm 1 and 'training dataset' in the Problem Formulation section but does not specify any particular train/validation/test splits (e.g., percentages, sample counts, or citations to predefined splits) needed for reproduction. It only states the total number of samples n = 10,000 for Mediamill. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions 'CPU runtime' as a metric. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For both MSG and MEG we set the step size at iteration t to be t = 0.1 p. The target dimensionality in our experiments is k 2 {1, 2, 4}. To ensure that the problem is well-conditioned, we add λI for λ = 0.1 to the empirical estimates of the covariance matrices on Mediamill dataset. |