Latent Semantics Encoding for Label Distribution Learning
Authors: Suping Xu, Lin Shang, Furao Shen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | The pseudo codes of LSE-LDL are presented in Algorithm 1 and Algorithm 2, which correspond to the training phase and the testing phase, respectively. |
| Open Source Code | No | The paper does not provide concrete access to its own source code. It only mentions that 'All the codes of above compared algorithms are shared by original authors'. |
| Open Datasets | Yes | The 15 data sets are coming from LDL website (http://ldl.herokuapp.com/download). |
| Dataset Splits | Yes | On each data set, ten-fold cross-validation is employed for the performance evaluation, and the mean value and the standard deviation of ten experimental results are respectively recorded. |
| Hardware Specification | Yes | All the experiments were carried out on a workstation equipped with an Intel Core i7 6850K CPU (3.60 GHz) and 32.00 GB memory. |
| Software Dependencies | Yes | We implement all LDL algorithms in Matlab R2017b. |
| Experiment Setup | Yes | In LSE-LDL, to model the local geometry structures in the latent semantic feature space, σ and ρ are set to be 0.05 and 1% of training instances, respectively. The number of selected features Q m. The regularization parameters in LSE-LDL are tuned with a grid-search strategy by varying their values in the range of t0.001, 0.01, 0.1, 1.0, 10u. The maximum number of iterations is 5000, and the small positive constant ϵ 0.0001. |