Exact Subspace Clustering in Linear Time
Authors: Shusen Wang, Bojun Tu, Congfu Xu, Zhihua Zhang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, empirical results verify our theoretical analysis. Our algorithm is based on very simple ideas, yet it is the only linear time algorithm with noiseless or noisy recovery guarantee. Finally, empirical results verify our theoretical analysis. Finally we conduct empirical comparisons between our method and three state-of-the-art methods on both synthetic and real-world data. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Zhejiang University, Hangzhou, China {wss, tubojun, xucongfu}@zju.edu.cn Zhihua Zhang Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China zhihua@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Exact Subspace Clustering in Linear Time and Algorithm 2 Data Selection are provided. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their proposed method is publicly available. |
| Open Datasets | Yes | We also evaluate the subspace clustering methods on real-world datasets the Hopkins155 Database and the Pen Based Handwritten Digit Dataset which are described in Table 1. (Tron and Vidal 2007) (Alimoglu and Alpaydin 1996) |
| Dataset Splits | No | The paper does not explicitly provide details about specific training/validation/test dataset splits, percentages, or explicit sample counts for reproduction. |
| Hardware Specification | Yes | We conduct experiments on a workstation with Intel Xeon 2.4GHz CPU, 24GB memory, and 64bit Windows Server 2008 system. |
| Software Dependencies | No | The paper states 'Our method is implemented in MATLAB' but does not provide a specific version number for MATLAB or any other software dependencies with their versions. |
| Experiment Setup | No | The paper mentions that parameters are 'tuned best for each input dataset' and describes optional settings for data selection (e.g., 'min{20r, n} instances are selected', 'uniformly at random'), but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) for the models or detailed system-level training settings. |