Fast Lasso Algorithm via Selective Coordinate Descent
Authors: Yasuhiro Fujiwara, Yasutoshi Ida, Hiroaki Shiokawa, Sotetsu Iwamura
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed experiments on the datasets of DNA, Protein, Reuters, TDT2, and Newsgroups to show the efficiency and effectiveness of our approach. |
| Researcher Affiliation | Collaboration | NTT Software Innovation Center, 3-9-11 Midori-cho Musashino-shi, Tokyo, 180-8585, Japan Center for Computational Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan |
| Pseudocode | Yes | Algorithm 1 Sling |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code specific to the Sling methodology described in the paper. |
| Open Datasets | Yes | We performed experiments on the datasets of DNA, Protein, Reuters, TDT2, and Newsgroups to show the efficiency and effectiveness of our approach. Details of the datasets are shown in Chih-Jen Lin s webpage2 and Deng Cai s webpage3. |
| Dataset Splits | Yes | This experiment performed leave-one-out cross validation in evaluating the prediction error in terms of the squared loss for the response. |
| Hardware Specification | Yes | We conducted all experiments on a Linux 2.70 GHz Intel Xeon server. |
| Software Dependencies | No | The paper states 'We implemented all approaches using GCC' but does not provide specific version numbers for GCC or any other software libraries. |
| Experiment Setup | Yes | We set λ1 = 1/n maxi | xi, y | and λK = 0.001λ1 by following the previous paper (Friedman, Hastie, and Tibshirani 2010). We constructed a sequence of K scores of tuning parameters decreasing from λ1 to λK on a log scale where K = 50. |