Partial Multi-Label Learning via Multi-Subspace Representation
Authors: Ziwei Li, Gengyu Lyu, Songhe Feng
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We conduct experiments on seven PML datasets, which are synthesized from widely-used MLL datasets including Emotions [Trohidis et al., 2008], Genbase [Diplaris et al., 2005], Medical [Pestian et al., 2007], Corel5k [Duygulu et al., 2002], Bibtex [Katakis et al., 2008], Eurlex-dc and Eurlexsm [Menc ıa and F urnkranz, 2008]. These datasets are added with redundant noise labels by the controlling parameter r. Here, r {1, 2, 3} represents the average number of false positive labels for training examples. Table 1 shows the characteristics of the experimental datasets. |
| Researcher Affiliation | Academia | Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology, Beijing Jiaotong University {18120390, lvgengyu, shfeng}@bjtu.edu.cn |
| Pseudocode | No | The paper describes the optimization steps in Section 3.2 with mathematical equations but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We conduct experiments on seven PML datasets, which are synthesized from widely-used MLL datasets including Emotions [Trohidis et al., 2008], Genbase [Diplaris et al., 2005], Medical [Pestian et al., 2007], Corel5k [Duygulu et al., 2002], Bibtex [Katakis et al., 2008], Eurlex-dc and Eurlexsm [Menc ıa and F urnkranz, 2008]. |
| Dataset Splits | Yes | Meanwhile, we use 10-fold cross-validation to train the model. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Parameters in MUSER method including α, β, γ are chosen from {10 3, 10 2, ..., 102, 103} with a grid search manner. Five widely-used multi-label metrics are employed to evaluate each comparing method... m and c are set to 50% of original feature and label space. |