Fused Orthogonal Alternating Least Squares for Tensor Clustering
Authors: Jiacheng Wang, Dan Nicolae
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate the accuracy and computational efficient implementation of the Fused-Orth-ALS algorithm by using both simulations and real datasets. |
| Researcher Affiliation | Academia | Jiacheng Wang Department of Statistics University of Chicago jiachengwang@uchicago.edu Dan Nicolae Department of Statistics University of Chicago nicolae@statistics.uchicago.edu |
| Pseudocode | Yes | Algorithm 1: Fused-Orth-ALS Algorithm |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Section 5 and supplementary material |
| Open Datasets | Yes | Experiments are conducted on two real datasets: the brain node structural connectivity from Human Connectome Project (HCP) 1 [25] and political relationships between nations 2 [13]. 1Dataset is available at http://www.humanconnectomeproject.org/ 2Dataset is available at http://www.charleskemp.com/code/irm.html. |
| Dataset Splits | No | The paper states that training details are in supplementary material but does not explicitly provide specific train/validation/test dataset splits (percentages, counts, or predefined splits) in the main text. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplementary material. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers (e.g., 'PyTorch 1.9' or 'scikit-learn 0.24'), in the main text. |
| Experiment Setup | Yes | We set d = 20, d3 = 48, µ = 1, vary σ {0, 0.25, 0.5, 0.75, 1} |