Upper bounds for Model-Free Row-Sparse Principal Component Analysis
Authors: Guanyi Wang, Santanu Dey
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results on both artificial and real cases are reported to demonstrate the advantages of our method. |
| Researcher Affiliation | Academia | 1H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, USA. Correspondence to: Guanyi Wang <gwang93@gatech.edu>, Santanu Dey <santanu.dey@isye.gatech.edu>. |
| Pseudocode | Yes | Algorithm 1 Local Search Method |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions 'The first two biological data sets (Eisen-1, Eisen-2) [d 300] are collected from ?. The Colon cancer data set [d = 500] is from ?. The Lymphoma data set [d = 500] is from ?. The final instance two real instances is collected from Reddit [d = 1000, 2000].' However, the citations are missing or generic (e.g., '?'), and no specific links or repository information are provided for public access. Artificial instances are generated, not from public datasets. |
| Dataset Splits | No | The paper describes the datasets used but does not provide specific details on how the data was split into training, validation, or test sets for reproducibility. |
| Hardware Specification | Yes | All numerical experiments are implemented on Mac Book Pro13 with 2GHz Intel Core i5 CPU and 8GB 1867MHz LPDDR3 Memory. |
| Software Dependencies | Yes | The SOCIP-impl model was solved using Gurobi 7.0.2. |
| Experiment Setup | Yes | Given φ a threshold parameter, and the size of |J+|, set the revised piecewise linear upper approximation (PLA ) constraints as, (g, ξ, η) : j vi, (j, i) [d] [r] gji = N ℓ= N SOS-II ji, (j, i) J+ [r] ξji = N ℓ= N SOS-II Thus the implemented version of SOCIP is j J+(λj φ) r i=1 ξji s + rφ s.t V C , (g, ξ, η) PLA (s-var), (sparse), (cut), (sym-1), (sym-2) (SOCIP-impl) |