Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation
Authors: Ze-Sen Chen, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang3553-3560
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China 3You Ku Cognitive and Intelligent Lab, Alibaba Group, Hangzhou, China 4Collaborative Innovation Center of Wireless Communications Technology, China |
| Pseudocode | Yes | Table 1 summarizes the complete procedure of GRADIS. Table 1: The pseudo-code of GRADIS. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | To thoroughly evaluate the performance of comparing approaches, seven benchmark data sets are collected for experimental studies including emotions (Trohidis et al. 2008), yeast (Elisseeff and Weston 2002), Corel5k (Duygulu et al. 2002), Pascal (Everingham et al. 2010), Mirflickr (Huiskes and Lew 2008), Youku25k and Youku50k (Wu et al. 2019). |
| Dataset Splits | Yes | Ten-fold cross-validation is performed on each data set, where the mean metric value as well as standard deviation are recorded. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods used (e.g., spectral clustering, ML-KNN, LIFT, F2L21F, LSAMML) but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | As shown in Table 1, parameters of GRADIS are set as k = 8, α = 0.95, γ = 0.1 and η = 0.1. For the comparing approaches, parameters suggested in respective literatures (Zhang and Zhou 2007; Zhang and Wu 2015; Zhu, Li, and Zhang 2016; Zhang et al. 2018) are used for experimental studies. the iterative procedure terminates when F(t) does not change or the maximum number of iterations (i.e. 30) is reached. Furthermore, the parameter σ in Eq.(1) is fixed to be 1. |