Unsupervised Anomaly Detection in The Presence of Missing Values
Authors: Feng Xiao, Jicong Fan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on datasets with manually constructed missing values and inherent missing values demonstrate that our proposed method effectively mitigates the imputation bias and surpasses the baseline methods significantly. |
| Researcher Affiliation | Academia | 1The Chinese University of Hong Kong, Shenzhen, China 2Shenzhen Research Institute of Big Data, Shenzhen, China |
| Pseudocode | No | The paper describes the proposed method and its implementation details in Section 3, but it does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | Yes | The source code of our method is available at https:// github.com/jicongfan/Im AD-Anomaly-Detection-With-Missing-Data. |
| Open Datasets | Yes | We compare Im AD with impute-then-detect methods on 11 publicly available tabular datasets from various fields... The statistics of all datasets are in Table 1 and a detailed description of all datasets is in Appendix J. |
| Dataset Splits | No | In all experiments, only incomplete normal data are used in the training stage, but there are both incomplete normal and abnormal data during the inference. |
| Hardware Specification | Yes | ALL experiments were conducted on 20 Cores Intel(R) Xeon(R) Gold 6248 CPU with one NVIDIA Tesla V100 GPU, CUDA 12.0. |
| Software Dependencies | Yes | ALL experiments were conducted on 20 Cores Intel(R) Xeon(R) Gold 6248 CPU with one NVIDIA Tesla V100 GPU, CUDA 12.0. |
| Experiment Setup | Yes | We use MLPs to construct the three modules of Im AD, Adam [Kingma and Ba, 2015] as the optimizer and set coefficient η of entropy regularization term in Sinkhorn distance to 0.1 in all experiments. Other experimental hyper-parameters are provided in Appendix J. Sensitivity analysis of hyper-parameters is provided in Appendix I. |