Dual-Regularized Multi-View Outlier Detection
Authors: Handong Zhao, Yun Fu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on five datasets with different outlier settings. The consistent superior results to other stateof-the-art methods demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Handong Zhao1 and Yun Fu1,2 1 Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115 2 College of Computer and Information Science, Northeastern University, Boston, USA, 02115 {hdzhao,yunfu}@ece.neu.edu |
| Pseudocode | Yes | Algorithm 1. Optimization Solution of Problem (2) and Algorithm 2. DMOD for Multi-view Outlier Detection are provided, outlining the steps for the proposed methods. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Among them, four are from UCI Machine Learning Repository1, i.e. iris, breast, ionosphere, and letter. The fifth one Vis Nir is from BUAA database [Di Huang and Wang, 2012]. |
| Dataset Splits | No | The paper describes how outliers are generated and how performance is evaluated across different outlier ratios. However, it does not specify explicit train/validation/test splits for model development or a cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Three parameters β, γ and K are set to 0.5, 0.1 and 3, respectively. The intrinsic dimension K of iris dataset is 3 since it has three classes. Therefore we make the evaluation in the range of [1, 8]. The average AUCs in three different settings are relatively steady when γ = {10 3, 10 2, 10 1, 100}. In practical, we choose γ = 0.1 as default for all experiments. |