Large-Scale Multi-View Subspace Clustering in Linear Time
Authors: Zhao Kang, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, Zenglin Xu4412-4419
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods. |
| Researcher Affiliation | Academia | Zhao Kang,1 Wangtao Zhou,1 Zhitong Zhao,1 Junming Shao,1 Meng Han,2 Zenglin Xu1,3 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, China 2School of Management and Economics, University of Electronic Science and Technology of China, China 3Centre for Artificial Intelligence, Peng Cheng Lab, Shenzhen 518055, China {zkang, zlxu}@uestc.edu.cn |
| Pseudocode | Yes | Algorithm 1: LMVSC algorithm |
| Open Source Code | Yes | Code is available at https://github.com/sckangz/LMVSC |
| Open Datasets | Yes | We perform experiments on several benchmark data sets: Handwritten, Caltech-101, Reuters, NUS-WIDE-Object. Specifically, Handwritten consists of handwritten digits of 0 to 9 from UCI machine learning repository. Caltech-101 is a data set of images for object recognition. [...] We choose the famous MNIST database, which consists of gray-scale images of size 28 × 28. [...] RCV1 consists of newswire stories from Reuters Ltd. Cov Type contains instances for predicting forest cover type from cartographic variables. |
| Dataset Splits | No | The paper mentions tuning parameters and searching ranges for `m` and `α` to obtain the best performance, which implies a validation process. However, it does not explicitly provide details about train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | Yes | All our experiments are implemented on a computer with a 2.6GHz Intel Xeon CPU and 64GB RAM, Matlab R2016a. |
| Software Dependencies | Yes | All our experiments are implemented on a computer with a 2.6GHz Intel Xeon CPU and 64GB RAM, Matlab R2016a. |
| Experiment Setup | Yes | We search anchor number m in the range [k, 50, 100]. [...] We select α from the range [0.001, 0.01, 0.1, 1, 10]. |