Robust Inverse Covariance Estimation under Noisy Measurements
Authors: Jun-Kun Wang, Shou-de Lin
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on time series prediction and classification under noisy condition demonstrate the effectiveness of the approach. |
| Researcher Affiliation | Collaboration | Jun-Kun Wang WANGJIM123@GMAIL.COM Intel-NTU, National Taiwan University, Taiwan Shou-de Lin SDLIN@CSIE.NTU.EDU.TW Intel-NTU, National Taiwan University, Taiwan |
| Pseudocode | Yes | Algorithm 1 Adjusting the inverse covariance that guarantees positive semi-definiteness |
| Open Source Code | Yes | The codes to reproduce the experiments are available on the first author s page https://sites.google.com/site/wangjim123. |
| Open Datasets | Yes | 1) Stock: The data are downloaded from Yahoo Finance... 2) Temperature (medium variable size): The data are downloaded from National Oceanic and Atmospheric Administration (NOAA) 1... 3) Temperature (large variable size)... four datasets, all are available on http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | The last 30 trading days are reserved for testing; the second to last 30 days are for validation; and the remaining data are for training. ... For each dataset, we random split data 5 times that 80 percent of data are for cross-validation and the remaining for testing. |
| Hardware Specification | Yes | Our experiment is run on a machine with dual core 2.66 GHz (INTEL E5500) and 32GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We use grid search to tune the regularization parameters. For our method, denote c as a vector whose entries are perturbation bound cg, the regularization vector is searched by c times [10 8, 10 7, . . . , 102] over the grid. ... input features contain previous three historic values... We compare our method with 1) the work of Hsieh (2011)... and 2) the work of Yuan (2010)... |