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].
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. |