Sinkhorn Regression
Authors: Lei Luo, Jian Pei, Heng Huang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the five publicly available microarray data sets and one mass spectrometry data set demonstrate the effectiveness and robustness of our method. |
| Researcher Affiliation | Collaboration | 1 JD Finance America Corporation, Mountain View, CA, USA 2Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA 3Department of Computing Science, Simon Fraser University, Canada |
| Pseudocode | Yes | Algorithm 1 Solving (13) via Alternating Optimization |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code. |
| Open Datasets | Yes | We use five publicly available microarray data sets and one Mass Spectrometry (MS) datasets: ALLAML data set [Fodor, 1997], the malignant glioma (GLIOMA) data set [Nutt et al., 2003], the human lung carcinomas (LUNG) data set [Bhattacharjee et al., 2001], Human Carcinomas (Carcinomas) data set [Yang et al., 2006], Prostate Cancer gene expression (Prostate-GE) data set [Singh et al., 2002] for microarray data; and Prostate Cancer (Prostate-MS) [Petricoin III et al., 2002] for MS data. |
| Dataset Splits | Yes | To be fair, the Support Vector Machine (SVM) classifier is employed to these data sets, using 5-fold cross-validation for all compared methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | For the results reported in the above subsection, we do not tune the parameter γ and µ and only set them as: γ = 0.01 and µ = 0.1. Better results may be achieved with tuning it. In this subsection, we will discuss sensitivity of parameter λ. Here, we take the top 20 features as an example on all data sets. The detailed results are shown in Table 3. It can be found the best results of our method mainly lie in the interval [0.01 0.5]. But in Table 1 and 2, we choose λ = 0.1. |