Semi-Analytic Resampling in Lasso
Authors: Tomoyuki Obuchi, Yoshiyuki Kabashima
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To examine approximation accuracy and efficiency, numerical experiments were carried out using simulated datasets. Moreover, an application to a real-world dataset, the wine quality dataset, is presented. |
| Researcher Affiliation | Academia | Tomoyuki Obuchi EMAIL Yoshiyuki Kabashima EMAIL Department of Mathematical and Computing Science Tokyo Institute of Technology 2-12-1, Ookayama, Meguro-ku, Tokyo, Japan |
| Pseudocode | No | The paper describes the algorithm steps through a series of mathematical equations (21a-21j) and explanatory text, rather than a formally structured pseudocode block or an algorithm box. |
| Open Source Code | Yes | MATLAB codes implementing the proposed method are distributed in (Obuchi, 2018). (Reference: Tomoyuki Obuchi. Matlab package of AMPR. https://github.com/T-Obuchi/AMPR_lasso_matlab, 2018.) |
| Open Datasets | Yes | Moreover, an application to a real-world dataset, the wine quality dataset, is presented. (Reference: M. Lichman. UCI machine learning repository, 2013. URL http://archive.ics.uci.edu/ ml.) |
| Dataset Splits | Yes | To this end, we plot the solution paths and the generalization errors estimated by 10-fold cross validation (CV) in Figure 9 in the cases with and without the noise variables. |
| Hardware Specification | Yes | For reference, it should be noted that all experiments below were conducted in a single thread on a single CPU of Intel(R) Xeon(R) E5-2630 v3 2.4GHz. |
| Software Dependencies | No | For all experiments involving numerical resampling, Glmnet (Friedman et al., 2010), implemented as an MEX subroutine in MATLAB R , was employed for solving (9), given a sample {λ, Dc}. Moreover, the proposed AMPR algorithm was implemented as raw code in MATLAB. No specific version numbers for MATLAB or Glmnet are provided. |
| Experiment Setup | Yes | The other parameters are set to be (N, α, ρ0, σ2 ξ) = (1000, 0.5, 0.2, 0.01). (Figure 1 Caption) and To introduce correlations into the simulated dataset described above in a systematic way, we generate our covariates {xi}N i=1 in the following manner: As a common component we first generate a vector xcom RM each component of which is i.i.d. from N(0, 1/N); choose a number 0 rcom < 1 controlling the ratio of the common component and generate a binary vector mi each component of which independently takes 1 with probability rcom; take another vector xi RM each component of which is i.i.d. from N(0, 1/N), and generate a covariate vector xi as a linear combination between xcom and xi with using mi as a mask. |