Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
Authors: Tomoharu Iwata, Makoto Yamada
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the proposed model quantitatively by using 11 data sets, which we obtained from the LIBSVM data sets [7]. For the evaluation measurement, we used the area under the ROC curve (AUC). Figure 2 shows AUCs with different rates of anomalies using 11 two-view data sets, which are averaged over 50 experiments. The proposed model achieved the best performance with eight of the 11 data sets. In the experiments, we confirmed that the proposed model could perform much better than existing methods for detecting multi-view anomalies. |
| Researcher Affiliation | Collaboration | Tomoharu Iwata NTT Communication Science Laboratories iwata.tomoharu@lab.ntt.co.jp Makoto Yamada Kyoto University makoto.m.yamada@ieee.org |
| Pseudocode | No | The paper describes the inference procedures for the proposed model in detail but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluated the proposed model quantitatively by using 11 data sets, which we obtained from the LIBSVM data sets [7]. |
| Dataset Splits | No | The paper mentions 'We generated two views by randomly splitting the features' but does not provide specific details on train, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation strategy). |
| Hardware Specification | No | The paper states 'The computational time needed for PCCA was 2 sec, and that needed for the proposed model was 35 sec with wine data', but it does not specify any hardware details like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., programming language versions, library versions, or specific solver versions) required to reproduce the experiments. |
| Experiment Setup | Yes | In the proposed model, we used γ = 1, a = 1, and b = 1 for all experiments. The number of iterations for the Gibbs sampling was 500, and the anomaly score was calculated by averaging over the multiple samples. |