Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Robust Spectral Clustering with Rank Statistics
Authors: Joshua Cape, Xianshi Yu, Jonquil Z. Liao
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical examples illustrate and support our theoretical findings. Additionally, for a data set consisting of human connectomes, our approach yields parsimonious dimensionality reduction and improved recovery of ground-truth neuroanatomical cluster structure. Sections 6 and 7 are dedicated to 'Numerical Examples' and 'Connectome Data Analysis' respectively, describing simulations and data analysis on real datasets. |
| Researcher Affiliation | Academia | Joshua Cape EMAIL Department of Statistics University of Wisconsin Madison Madison, WI 53706, USA; Xianshi Yu EMAIL Department of Computer Sciences University of Wisconsin Madison Madison, WI 53706, USA; Jonquil Z. Liao EMAIL Department of Statistics University of Wisconsin Madison Madison, WI 53706, USA. All authors are affiliated with the University of Wisconsin Madison, which is an academic institution, and their emails use the .edu domain. |
| Pseudocode | Yes | The paper contains 'Algorithm 1 Pass-to-ranks (PTR) rank-transform procedure', 'Algorithm 2 Spectral embedding and clustering with matrices of rank statistics', and 'Algorithm 3 Spectral clustering with approximate k-means; see Lei and Rinaldo (2015)'. |
| Open Source Code | Yes | Code for this paper is available online on the first author s webpage. |
| Open Datasets | Yes | For numerical examples, the paper states: 'Additionally, for a data set consisting of human connectomes, our approach yields parsimonious dimensionality reduction and improved recovery of ground-truth neuroanatomical cluster structure.' In Section 7, 'Connectome Data Analysis', it specifies: 'We consider the data set DS01876, publicly available from Johns Hopkins University and documented in more detail in Lawrence et al. (2021).' |
| Dataset Splits | No | The paper mentions generating data for simulations and using a 'ground-truth clustering given by {LG, LW, RG, RW}' for evaluating connectome data. However, it does not specify explicit training/test/validation dataset splits (e.g., percentages or counts) for model training or evaluation in the traditional sense of machine learning experiments. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., GPU models, CPU types, memory amounts) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) that are needed to replicate the experiments. |
| Experiment Setup | Yes | The paper provides details for numerical examples in Section 3.3, such as 'K = 2 blockmodel', 'n = 1000', 'equal block sizes n1 = n2 = n/K = 500', 'ϵ = 0.01', 'µ11 = µ22 = 2, µ12 = 1, and σ11 = σ12 = σ22 = 3'. In Section 7, for connectome data analysis, it states 'Using k-means clustering with four clusters'. |