Sparse PCA with Oracle Property
Authors: Quanquan Gu, Zhaoran Wang, Han Liu
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the theoretical results by numerical experiments on synthetic datasets. |
| Researcher Affiliation | Academia | Quanquan Gu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA qgu@princeton.edu Zhaoran Wang Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA zhaoran@princeton.edu Han Liu Department of Operations Research and Financial Engineering Princeton University Princeton, NJ 08544, USA hanliu@princeton.edu |
| Pseudocode | Yes | Algorithm 1 Solving Convex Relaxation (5) using ADMM. |
| Open Source Code | No | No explicit statement providing concrete access to source code for the described methodology (e.g., repository link, code release statement) was found in the paper. |
| Open Datasets | No | The paper states: 'We generate two synthetic datasets via designing two covariance matrices.' It does not provide specific access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | Yes | The regularization parameter λ in our estimators as well as Fantope SPCA is tuned by 5-fold cross validation on a held-out dataset. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided in the paper. |
| Experiment Setup | Yes | We sample n = 80 i.i.d. observations from a normal distribution N(0, Σ) with p = 128. Both of our estimators use MCP penalty... In particular, we set b = 3. For Convex SPCA, we set τ = 2b. The regularization parameter λ in our estimators as well as Fantope SPCA is tuned by 5-fold cross validation on a held-out dataset. |