Split LBI: An Iterative Regularization Path with Structural Sparsity
Authors: Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan Yao
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The utility and benefit of the algorithm are illustrated by applications on both traditional image denoising and a novel example on partial order ranking. Example 1. Consider two problems: standard Lasso and 1-D fused Lasso. In both cases, set n = p = 50, and generate X Rn p denoting n i.i.d. samples from N(0, Ip), ϵ N(0, In), y = Xβ + ϵ. Table 1: Mean AUC (with standard deviation) comparisons where Split LBI (1.4) beats genlasso. Left is for the standard Lasso. Right is for the 1-D fused Lasso in Example 1. Figure 2: Left is image denoising results by Split LBI. Right shows the AUC of Split LBI (blue solid line) increases and exceeds that of genlasso (dashed red line) as ν increases. |
| Researcher Affiliation | Academia | 1Peking University, 2Hong Kong University of Science and Technology |
| Pseudocode | No | The iterative algorithm is described by equations (1.4a), (1.4b), (1.4c) but is not formatted as a structured pseudocode block or explicitly labeled as 'Algorithm'. |
| Open Source Code | No | The paper mentions that the 'R package genlasso can be found in CRAN repository' in relation to a comparative method, but there is no statement or link indicating that the authors' own code for Split LBI is open-source or publicly available. |
| Open Datasets | No | For Example 1, data is synthetically generated: 'set n = p = 50, and generate X Rn p denoting n i.i.d. samples from N(0, Ip), ϵ N(0, In), y = Xβ + ϵ.'. For image denoising, 'The original image is resized to 50 50... Some noise is added'. For partial order ranking, data was 'collected n = 134 pairwise comparison game results... from various important championship', but no information about public access (link, citation, repository) is provided for this collected data. |
| Dataset Splits | No | The paper mentions conducting '100 independent experiments' and calculating 'mean AUC' but does not specify a train/validation/test split for a persistent dataset. The experiments appear to involve generating new data for each run or using collected data without a traditional split for model development and evaluation stages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions the 'R package genlasso' as a comparative tool but does not specify its version. It does not list any other software dependencies with version numbers for their own implementation. |
| Experiment Setup | Yes | Parameter κ should be large enough according to (2.12). Moreover, step size α should be small enough to ensure the stability of Split LBI. When ν, κ are determined, α can actually be determined by α = ν/(κ(1 + νΛ2 X + Λ2 D)) (see (C.6) in Supplementary Information). For Example 1: 'κ = 200 and ν {1, 5, 10}'. For Image Denoising: 'Set ν = 180, κ = 100. ... Here ν {1, 20, 40, 60, . . . , 300}'. For Partial Order Ranking: 'ν = 1 and κ = 100'. |