Online Multitask Relative Similarity Learning
Authors: Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present our empirical studies on two real datatsets for evaluating both the efficacy and efficiency of the proposed algorithms. |
| Researcher Affiliation | Collaboration | 1Institute of High Performance Computing, A*STAR, Singapore 2Articial Intelligence Department, Ant Financial Services Group, China 3Institute for Infocomm Research, A*STAR, Singapore 4School of Information Systems, SMU, Singapore 5School of Computer Science and Engineering, NTU, Singapore |
| Pseudocode | Yes | Algorithm 1 OMTRSL: The proposed algorithm for Online Multi-Task Relative Similarity Learning. Algorithm 2 OMTRSL-Active: The proposed algorithm for Active Online Multi-Task Relative Similarity Learning. |
| Open Source Code | No | The paper mentions a link for a comparison algorithm (mt LMNN) but does not provide any concrete access or explicit statement about releasing its own source code for OMTRSL or OMTRSL-Active. |
| Open Datasets | Yes | The performances of the proposed methods are evaluated on two real-world datasets: the Isolet spoken alphabet recognition dataset [Fanty and Cole, 1990] and the news20 dataset 1. 1Available on the LIBSVM Machine Learning Repository. |
| Dataset Splits | Yes | On both datasets, we used standard 5-fold cross validation for evaluation, in which 80% of the data are used for training, and the remaining 20% are used for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It only mentions implementation language (Matlab). |
| Software Dependencies | No | The paper states "we implement our algorithms with pure Matlab language" but does not specify a version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Input: Parameters C > 0 and b > 0 (for OMTRSL and OMTRSL-Active). Input: Parameters C > 0, b > 0 and δ > 0 (for OMTRSL-Active). An empirical setting of C is 1. |