Cone-Constrained Principal Component Analysis
Authors: Yash Deshpande, Andrea Montanari, Emile Richard
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed numerical experiments on synthetic data generated according to the model (1) and with signal v0 = u(n, nε) as defined in the previous section. We provide in the Appendix formulas for the value of limn v0, bv ML , which correspond to continuous black lines in the Figure 1. We compare these predictions with empirical values obtained by running AMP. |
| Researcher Affiliation | Academia | Yash Deshpande Electrical Engineering Stanford University Andrea Montanari Electrical Engineering and Statistics Stanford University Emile Richard Electrical Engineering Stanford University |
| Pseudocode | No | The algorithms are described using mathematical equations (6), (7), and AMP, but are not presented in formal pseudocode or algorithm blocks. |
| Open Source Code | No | No statement or link providing concrete access to source code for the methodology was found. |
| Open Datasets | No | The paper uses 'synthetic data generated according to the model (1)' but provides no access information (link, citation, or repository) for this data. |
| Dataset Splits | No | No specific dataset split information (e.g., percentages, sample counts, or predefined splits with citations) for training, validation, or test sets was found. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or operating systems) were provided. |
| Experiment Setup | Yes | We generated samples of size n = 104, sparsity level ε {0.001, 0.1, 0.8}, and signal-to-noise ratios β {0.05, 0.10, . . . , 1.5}. In each case we run AMP for t = 50 iterations and plot the empirical average of bvt, v0 over 32 instances. |