Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization
Authors: Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo5508-5515
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction. |
| Researcher Affiliation | Academia | 1College of Computer and Information Science, Southwest University, Chongqing, China 2Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China 3Department of Computer Science, George Mason University, Fairfax, USA 4School of Information Technology, Deakin University, Geelong, Australia 5School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China |
| Pseudocode | No | Due to page limit, the optimization procedures of these variables are provided in the Supplementary file. |
| Open Source Code | Yes | The Supplementary file and code of M3Lcmf are available at http://mlda.swu.edu.cn/codes.php?name=M3Lcmf. |
| Open Datasets | Yes | Nine publicly available multi-instance multi-label datasets from different domains are used for the experiments. The details of the datasets are given in Table 2. The first five datasets are collected from http://lamda.nju.edu.cn/CH.Data. ashx and http://github.com/hsoleimani/MLTM/tree/master/ Data. |
| Dataset Splits | No | The paper states: 'We randomly partition the samples of each dataset into a training set (70%) and a testing set (30%)', but it does not specify a validation set or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Both λ1 and λ2 are fixed to 1000, and the low-rank size of Gi (i {1, 2, 3}) is fixed to 140. The input parameters of these comparing methods are specified (or optimized) as suggested by the authors in their code or papers, and the setting of the parameters for M3Lcmf will be investigated later. |