Robust Subspace Clustering via Thresholding Ridge Regression
Authors: Xi Peng, Zhang Yi, Huajin Tang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies show that TRR outperforms the state-of-the-art methods with respect to clustering quality, robustness, and time-saving. In this section, we investigate the performance of TRR for robust face clustering with respect to clustering quality, robustness, and computational efficiency. |
| Researcher Affiliation | Academia | 1Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632 2College of Computer Science, Sichuan University, Chengdu 610065, P.R. China. |
| Pseudocode | Yes | Algorithm 1: Robust Subspace Clustering via Thresholding Ridge Regression |
| Open Source Code | Yes | 1The codes can be downloaded at the authors website http://www.machineilab.org/users/pengxi/. |
| Open Datasets | Yes | We used two popular facial databases, i.e., Extended Yale Database B (Georghiades, Belhumeur, and Kriegman 2001) (Ex Yale B) and AR database (Martinez and Benavente 1998). |
| Dataset Splits | No | The paper mentions selecting 'a half of samples to corrupt' for specific tests and analyzing 'the first L subjects', but it does not provide specific train/validation/test split percentages, sample counts, or citations to predefined splits for general model reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions software like Node XL and k-means clustering but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the SSC algorithm, we experimentally found an optimal α from 1 to 50 with an interval of 1. For LRR, the optimal λ was found from 10-6 to 10 as suggested in (Liu et al. 2013). For LSR and TRR, the optimal λ was chosen from 10-7 to 1. Moreover, a good k was found from 3 to 14 for TRR and from 1 to 100 for LLE-graph. TRR has two parameters, the balance parameter λ and the thresholding parameter k. The values of these parameters depend on the data distribution. |