Unfolding-Model-Based Visualization: Theory, Method and Applications
Authors: Yunxiao Chen, Zhiliang Ying, Haoran Zhang
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation studies and real data examples are presented in Sections 4 and 5, respectively. We illustrate the proposed method through two applications, one on movie rating and the other on senate roll call voting. |
| Researcher Affiliation | Academia | Yunxiao Chen EMAIL Department of Statistics London School of Economics and Political Science London, WC2A 2AE, UK Zhiliang Ying EMAIL Department of Statistics Columbia University New York, NY 10027, USA Haoran Zhang EMAIL Shanghai Center for Mathematical Sciences Fudan University Shanghai, 200433, China |
| Pseudocode | Yes | Algorithm 1 (Alternating minimization algorithm) Input: Data (yij)N J, pre-specified dimension K+, constraint M, iteration number m = 1, and the initial values θ(0) 1 , ..., θ(0) N and a(0) 1 , ..., a(0) J in RK+. Algorithm 2 (Two-step procedure for bi-cluster analysis) Step 1: Apply Algorithm 1 and obtain estimates {ˆθ1, ..., ˆθN, ˆa1, ..., ˆa J}. |
| Open Source Code | Yes | 1. An R package has been developed and can be downloaded from https://github.com/hrzhang16/mmdu. |
| Open Datasets | Yes | The movie dataset is a subset from the famous Movie Lens dataset (Harper and Konstan, 2016). The dataset can be downloaded from https://grouplens.org/datasets/movielens/100k/. This dataset contains the voting records from 100 senators to 675 roll calls in years 2003 and 20044. The dataset can be downloaded from https://legacy.voteview.com/dwnl.htm. |
| Dataset Splits | No | The paper describes using a subset of the Movie Lens dataset containing 943 users and 338 movies, and a senate roll call voting dataset from the 108th congress involving 100 senators and 675 roll calls. It mentions generating 100 independent datasets for simulation studies. However, it does not specify any training, validation, or test splits for these datasets, nor does it refer to standard predefined splits. |
| Hardware Specification | Yes | 2. All the computation is conducted on a single Intel Gold 6130 core. |
| Software Dependencies | No | The paper mentions "our implementation of Algorithm 1 in statistical software R" and "An R package has been developed and can be downloaded from https://github.com/hrzhang16/mmdu." However, no specific version numbers for R or any other software packages or libraries are provided. |
| Experiment Setup | Yes | In the application of Algorithm 1, the constraint M is set to 1.5. using ten random starting points and then choosing the result that gives the largest likelihood function value. The results presented below are based on the choice δ = 0.1, but we point out that other choices of δ (δ = 0.05, 0.15, 0.2) have also been tried which all lead to very similar results. The constraint constant M is set to 3.5 when applying Algorithm 1. |