Incomplete Label Distribution Learning
Authors: Miao Xu, Zhi-Hua Zhou
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the effectiveness of our proposal. |
| Researcher Affiliation | Academia | Miao Xu and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University Nanjing 210023, China {xum,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Incom LDL-prox |
| Open Source Code | No | All the codes are shared by original authors, and we use the default parameter suggested there, except that we tune the regularization parameter for the PTSVM algorithm using 10-folder cross-validation in the same way as in Incom LDL-prox. |
| Open Datasets | Yes | We evaluate the proposed algorithms for Incom LDL problem on 15 real data sets. ... Details of them can be found in [Geng, 2016]. Here we summarize their statistics in Table 1. |
| Dataset Splits | Yes | In Incom LDL-prox, the regularization parameter is selected from 2{ 10, 9,...,9,10} by cross-validation on training data. ... The value is measured by 10-folder cv shown in mean std form. |
| Hardware Specification | Yes | All the results were obtained on a Linux server with CPU 2.53GHz and 48GB memory. |
| Software Dependencies | No | We implement our approaches in Matlab. |
| Experiment Setup | Yes | In Incom LDL-prox, the regularization parameter is selected from 2{ 10, 9,...,9,10} by cross-validation on training data. Parameters γ and ϵ are set to be 2 and 10 5 respectively. The maximum iteration is set to be 100. In Incom LDL-admm, regularization parameter λ and number of maximum iteration are selected in the same way as Incom LDL-prox. ρ1 is simply set as 1 and all the variables are initialized to be all-zero. The stopping criterion parameters ϵabs and ϵrel are set as 10 4 and 10 2 as suggested in the survey [Boyd et al., 2011]. |