Exploiting Multi-Label Correlation in Label Distribution Learning
Authors: Zhiqiang Kou, Jing Wang, Jiawei Tang, Yuheng Jia, Boyu Shi, Xin Geng
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China 3 Chien-Shiung WU College, Southeast University, Nanjing 210096, China {zhiqiang kou, wangjing91, jwtang, yhjia, shiboyu, xgeng}@seu.edu.com |
| Pseudocode | Yes | Algorithm 1 The pseudo-code for TLRLDL and TKLRLDL |
| Open Source Code | Yes | 1https://github.com/users/zhiqiang-kou/projects/1 |
| Open Datasets | Yes | Experimental Datasets: The experiments are conducted on 16 real-world datasets with label distribution. The key characteristics of these datasets are summarized in Table 1. Geng collects the first 12 datasets [Geng, 2016]. Among these, the first eight (from Spoem to Alpha) are from the clustering analysis of genome-wide expression in Yeast Saccharomyces cerevisiae [Eisen et al., 1998]. The SJAFFE is collected from JAFFE [Lyons et al., 1998], and the SBU 3DFE is obtained from BU 3DFE [Yin et al., 2006]. The Gene is obtained from the research on the relationship between genes and diseases [Yu et al., 2012]. The Scene consists of multi-label images, where the label distributions are transformed from rankings [Geng and Xia, 2014]. Besides, the SCUT-FBP, M2B, and fbp5500 are about facial beauty perception [Ren and Geng, 2017]. The last one, RAF-ML is a facial expression dataset [Li and Deng, 2019] with six-dimension expression distribution. |
| Dataset Splits | Yes | We run each method for ten-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, etc.) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters of the methods are as follows. The suggested parameters are used for IIS-LLD, EDL-LRL, LDLLC, and LDL-LCLR. For LDLLDM, λ1, λ2, and λ3 are tuned from 10-3, . . . , 103 , and g is tuned from 1 to 14. For Incom LDL, λ is selected from the range 2-10, . . . , 210 , and ρ = 1. For Adam-LDL-SCL, λ1, λ2, and λ3 are tuned from the set 10-3, . . . , 103 , and m is tuned from 0 to 14. For TLRLDL and TKLRLDL, α, λ are tuned from {0.005, 0.01, 0.05, 0.1, 0.5, 1, 10}, T is selected from 0.1 to 0.5, and k is tuned from 0 to m. |