Multiple Partitions Aligned Clustering

Authors: Zhao Kang, Zipeng Guo, Shudong Huang, Siying Wang, Wenyu Chen, Yuanzhang Su, Zenglin Xu

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We conduct experiments on four benchmark data sets: BBCSport, Caltech7, Caltech20, Handwritten Numerals. Their statistics information is summarized in Table 1. We compare the proposed m PAC with several state-of-the-art methods from different categories
Researcher Affiliation Academia 1School of Computer Science and Engineering, University of Electronic Science and Technology of China 2School of Foreign Languages, University of Electronic Science and Technology of China zkang@uestc.edu.cn, {zpguo, huangsd, siyingwang}@std.uestc.edu.cn, {cwy, syz, zlx}@uestc.edu.cn
Pseudocode Yes Algorithm 1 Optimization for m PAC
Open Source Code Yes Our code is available: https://github.com/sckangz/mPAC
Open Datasets Yes We conduct experiments on four benchmark data sets: BBCSport, Caltech7, Caltech20, Handwritten Numerals. Their statistics information is summarized in Table 1.
Dataset Splits No The paper mentions conducting experiments on benchmark datasets and repeating methods 10 times but does not provide specific training, validation, or test dataset split percentages or sample counts.
Hardware Specification No The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory amounts) used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies with version numbers (e.g., Python version, library names with versions) needed to replicate the experiment.
Experiment Setup Yes Input: Multiview matrix X1, , Xv, cluster number c, parameters α, β, γ. ... Taking Caltech7 as an example, we demonstrate the influence of parameters to clustering performance. ... it becomes more robust to α and β when γ increases, which indicates the importance of partition alignment.