Orderly Subspace Clustering
Authors: Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi5264-5272
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer. |
| Researcher Affiliation | Academia | 1Graduate School of Information Science and Technology, The University of Tokyo, Japan 2Faculty of Science and Technology, Bournemouth University, UK 3School of Artificial Intelligence, Hebei University of Technology, China |
| Pseudocode | Yes | The whole procedure of OSC is summarized in Algorithm 1. Algorithm 1 Solving OSC |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | To demonstrate the effectiveness of OSC, we conducted experiments on a variety of datasets, including image datasets (Extended-Yale B, USPS), a document dataset (20newsgroups) and a motion sequence (Hopkins 155). |
| Dataset Splits | No | The paper describes how supervision data was selected ('randomly selected 10% data for each dataset, and then constructed 30 orderly relations'), but it does not specify explicit train/validation/test dataset splits for the entire datasets used for model evaluation. |
| Hardware Specification | Yes | All the experiments are done using Matlab 2017 in an Intel Core 2.50GHZ desktop. |
| Software Dependencies | Yes | All the experiments are done using Matlab 2017 in an Intel Core 2.50GHZ desktop. |
| Experiment Setup | Yes | For OSC, we varied the regularization parameters α and β within [0.1, 0.2, 0.3, 0.4, 0.5] and [0.01, 0.02, 0.03, 0.04, 0.05], respectively, and δ was fixed to be 0.002 for all the experiments. To construct orderly relations for OSC, we first randomly selected 10% data for each dataset, and then constructed 30 orderly relations for each selected data as in (Chang et al. 2014). [...] q was set as 5 according to (Gong et al. 2017). |