Auto-Weighted Multi-View Clustering for Large-Scale Data

Authors: Xinhang Wan, Xinwang Liu, Jiyuan Liu, Siwei Wang, Yi Wen, Weixuan Liang, En Zhu, Zhe Liu, Lu Zhou

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental To validate the effectiveness of AWMVC, we conduct extensive experiments on various datasets. The results demonstrate the efficiency and excellent performance of the proposed method.
Researcher Affiliation Academia 1College of Computer, National University of Defense Technology, Changsha, China 2College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China
Pseudocode Yes Algorithm 1: Auto-Weighted Multi-View Clustering for Large-Scale Data
Open Source Code Yes The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.
Open Datasets Yes Seven benchmark datasets are adopted to verify the promising performance of AWMVC, and the maximum number of samples used is more than 100,000. The datasets used in our experiment include Flower171, Aw A2, Caltech2563, MNIST4, VGGFace25, Tiny Image Net6, You Tube Face507. (Footnotes 1-7 provide URLs to these datasets).
Dataset Splits No The paper mentions using "k-means" and tuning hyperparameters with "gridsearch," but it does not provide explicit details on how the datasets were split into training, validation, and test sets.
Hardware Specification Yes All the experiments are conducted on a desktop computer with Intel(R) Core(TM) i910850K CPU and 96G RAM.
Software Dependencies No The paper mentions using k-means for clustering but does not specify software dependencies with version numbers like programming languages, libraries, or frameworks.
Experiment Setup Yes In our experiment, we simply set m = 3 and the dimensions range from k to mk. For methods with hyperparameters, we tune them with gridsearch recommended in their papers and report the best results.