Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection
Authors: Li Cheng, Yijie Wang, Xinwang Liu7099-7106
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Li Cheng, Yijie Wang , Xinwang Liu College of Computer, National University of Defense Technology, Changsha, China {chengli09, wangyijie, xinwangliu}@nudt.edu.cn |
| Pseudocode | Yes | Algorithm 1 Optimization of NCMOD |
| Open Source Code | Yes | All the source codes, including the proposed NCMOD, the competitors and datasets generation, are provided in the supplementary. |
| Open Datasets | Yes | Three widely used high dimensional benchmarks are used: MNIST, REUTERS, TTC, and the dimensionality of them are respectively 784, 2000 and 7507. |
| Dataset Splits | No | The paper describes how the datasets are generated and structured for outlier detection but does not specify a separate validation dataset split in terms of percentages or counts for hyperparameter tuning or model selection in the traditional sense of supervised learning. It mentions setting a threshold for F1-Score evaluation, but not a validation split. |
| Hardware Specification | Yes | The methods are all implemented in Python 3.4 executed at a PC in a 3.6GHz CPU with 16GB memory. |
| Software Dependencies | No | The paper states 'implemented in Python 3.4'. While Python 3.4 is a specific software version, it does not list any other key software components, libraries, or solvers with their version numbers, which would typically be needed for full reproducibility of ancillary software. |
| Experiment Setup | Yes | We set k = 8 and α = 1.0 as default. The threshold is set as the outlier ratio, i.e., 0.15. |