A Convex Optimization Framework for Bi-Clustering
Authors: Shiau Hong Lim, Yudong Chen, Huan Xu
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on both synthetic and real-world data are presented in Section 6. |
| Researcher Affiliation | Academia | National University of Singapore, 9 Engineering Drive 1, Singapore 117575, University of California, Berkeley, CA 94720, USA |
| Pseudocode | Yes | Algorithm 1 ADMM solver for Program (1) Input: W Rn1 n2, λ, b0, b1, ϵ Output: Y |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Our first dataset is the animal-feature data originally published by Osherson et al. (1991) and has been used in (Wulff et al., 2013) and (Kemp et al., 2006) for biclustering. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information for training, validation, or testing, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | The average computational time needed to finish one instance of program (1) in Matlab on a Core i5 desktop machine is also reported in Fig. 2. |
| Software Dependencies | No | The paper mentions 'Matlab' but does not provide specific version numbers for it or any other ancillary software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | We find that in practice, setting λ = 2n works well. The threshold for convergence is specified by ϵ. We find that ϵ = 10-4 is a good tradeoff between the speed of convergence and the quality of the solution. All our experiment results, unless otherwise stated, were based on λ = 2n and ϵ = 10-4. |