Multi-View Anomaly Detection: Neighborhood in Locality Matters
Authors: Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, Yuan Jiang4894-4901
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the proposed MUVAD approaches, we perform experiments on synthetic datasets that have no clusters, benchmark datasets and real world multi-view anomaly detection task. We compare the proposed MUVAD-QPR and MUVAD-FSR approaches with OCSVM (Sch olkopf et al. 2001), HOAD (Gao et al. 2011), CC (Liu and Lam 2012), MLRA (Li, Shao, and Fu 2015), DMOD (Zhao and Fu 2015) and CRMOD (Zhao et al. 2018). ...The results are reported in Tab. 1. Experimental results validate the superiority of the proposed MUVAD approaches. |
| Researcher Affiliation | Academia | Xiang-Rong Sheng, De-Chuan Zhan, Su Lu, Yuan Jiang National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China {shengxr, zhandc, lus, jiangy}@lamda.nju.edu.cn |
| Pseudocode | No | The paper describes its proposed methods mathematically and in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicitly state that the code for their methodology is publicly available. |
| Open Datasets | Yes | This experiment compares the proposed approaches with others on benchmark datasets from UCI Machine Learning Repository1 and real world multi-view applications. ...we employ three UCI datasets, Ionosphere, Vowel and Zoo for comparison. ...we also include two real world multi-view datasets, News M and News NG, which are extracted from the 20 Newsgroup datasets and have 3 views (Bisson and Grimal 2012). |
| Dataset Splits | No | The paper describes the datasets used and how anomalies were generated for evaluation, but it does not specify explicit training, validation, and test dataset splits with percentages, sample counts, or cross-validation details needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | As for MUVAD-QPR, we use t = 7, N0 = 0.9N, λ = 2000 as default parameters. As for MUVAD-FSR, we use t = 7, γ = 2000 as default parameters. |