Discrete Binary Coding based Label Distribution Learning
Authors: Ke Wang, Xin Geng
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on five real-world datasets demonstrate its superior performance over several state-of-the-art LDL methods with the lower time cost. |
| Researcher Affiliation | Academia | Ke Wang and Xin Geng MOE Key Laboratory of Computer Network and Information Integration, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {k.wang, xgeng}@seu.edu.cn |
| Pseudocode | Yes | Algorithm 1 DBC-LDL |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology or provide a link to a code repository. |
| Open Datasets | Yes | We conduct our experiments on five real-world datasets, namely M2B (Multi-Modality Beauty) [Nguyen et al., 2012], s-BU 3DFE (scores-Binghamton University 3D Facial Expression) [Zhou et al., 2015], Twitter LDL [Yang et al., 2017], Flickr LDL [Yang et al., 2017], and Ren-CECps [Quan and Ren, 2010], to evaluate our algorithm in terms of both accuracy and efficiency. |
| Dataset Splits | Yes | All the results are averaged over 10-fold cross validation in terms of both accuracy and time cost. |
| Hardware Specification | Yes | All the experiments are implemented using Matlab on a standard PC with a 2.30GHz Intel CPU and 12GB memory. |
| Software Dependencies | No | The paper mentions "implemented using Matlab" but does not provide a specific version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For the proposed DBC-LDL, we empirically set the parameter α = 104, β = 104 and γ = 10 2 . The code length in DBC-LDL and BC-LDL is same (i.e., 128 bits) for making a fair comparison, and k in DBC-LDL, BC-LDL and AA-k NN is chosen from {10, 20, , 50}. |