Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Authors: Xixian Chen, Michael R. Lyu, Irwin King
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the extensive experiments on synthetic and real-world datasets validate the superior property of our method and illustrate that it significantly outperforms the state-of-the-art algorithms.4. Empirical Studies In this section, we empirically verify the properties of the proposed method and demonstrate its superiority. We compare its estimation accuracy with that of Gauss-Inverse, Sparse, and Uni Sample-HD. We also report the time comparisons. |
| Researcher Affiliation | Academia | 1Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. 2Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. |
| Pseudocode | Yes | Algorithm 1 The proposed algorithm. |
| Open Source Code | No | The paper mentions implementing algorithms in C++ for comparison but does not provide a link or explicit statement about releasing the source code for their proposed methodology. |
| Open Datasets | Yes | We use nine publicly available real-world datasets (Chang & Lin, 2011; Blake & Merz, 1998; Amsaleg, 2010) |
| Dataset Splits | No | The paper uses synthetic and real-world datasets but does not explicitly provide details about training, validation, or test splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | Yes | We implement all algorithms in C++ and run them in a single thread mode on a standard workstation with Intel CPU@2.90GHz and 128GB RAM. |
| Software Dependencies | No | The paper states that algorithms are implemented in C++ but does not provide specific version numbers for any ancillary software, libraries, or solvers used. |
| Experiment Setup | Yes | The parameter selection on α is deferred to the appendix, and we empirically set α = 0.9. |