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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multiple Independent Subspace Clusterings
Authors: Xing Wang, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao, Maozu Guo5353-5360
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic datasets show that MISC can ο¬nd different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets. |
| Researcher Affiliation | Academia | 1College of Computer and Information Sciences, Southwest University, Chongqing, China 2Department of Computer Science, George Mason University, Fairfax, USA 3Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Hubei, China 4School of Electrical and Information Engineering, Beijing University Of Civil Engineering and Architecture, Beijing, China Email: EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 MISC: Multiple Independent Subspace Clusterings |
| Open Source Code | Yes | The code for MISC is available at http://mlda.swu.edu.cn/codes.php?name=MISC. |
| Open Datasets | Yes | The second and third synthetic datasets are collected from the Fundamental Clustering Problem Suite (FCPS)2. http://www.uni-marburg.de/fb12/datenbionik/downloads/FCPS |
| Dataset Splits | No | The paper describes the datasets used but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We choose the Gaussian heat kernel as the kernel function and the kernel width is set to the standard variance Ο = sqrt(Pn i=1 X i X 2 /n). Following the set of GNMF in (Cai et al. 2011), we use 0-1 weighting and adopt the neighborhood size Ο΅ = 5 to compute the graph adjacency matrix P, and then set Ξ» = 10 in Eq. (8). We also set the number of subspaces as 2 and the number of clusters as that of true labels of CMUface and Web KB datasets, respectively. |