FISH-MML: Fisher-HSIC Multi-View Metric Learning
Authors: Changqing Zhang, Yeqinq Liu, Yue Liu, Qinghua Hu, Xinwang Liu, Pengfei Zhu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The learned metrics can improve multi-view classification, and experimental results on real-world datasets demonstrate the effectiveness of the proposed method. We conduct experiments on four real-world datasets and compare our FISH-MML with existing state-of-the-art methods in terms of diverse evaluation measures. |
| Researcher Affiliation | Academia | Changqing Zhang1, Yeqing Liu1, Yue Liu1, Qinghua Hu1 , Xinwang Liu2 and Pengfei Zhu1 1School of Computer Science and Technology, Tianjin University, Tianjin, China 2School of Computer, National University of Defense Technology, Changsha, China {zhangchangqing, yeqing, liuyue76, huqinghua, zhupengfei}@tju.edu.cn, 1022xinwang.liu@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The datasets employed are as follows: handwritten1 contains 2000 images of 10 classes from number 0 to 9. ... Caltech101-72 contains a subset of images from Caltech101... MSRA [Liu et al., 2010] contains 210 images... football3 consists of 248 English Premier League football players... 1https://archive.ics.uci.edu/ml/datasets/Multiple+Features 2http://www.vision.caltech.edu/Image Datasets/Caltech101/ 3http://mlg.ucd.ie/aggregation/index.html |
| Dataset Splits | Yes | Each dataset is randomly partitioned into 80% for training and 20% for testing. Then 20% samples are randomly selected from the training set as validation set for parameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We select the value from {0.001, 0.01, 0.1, 1, 10, 100, 1000} for λ1 and λ2. Uniformly, we set the number of nearest neighborhoods to 5 for all methods on each dataset. |