Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
FISH-MML: Fisher-HSIC Multi-View Metric Learning
Authors: Changqing Zhang, Yeqinq Liu, Yue Liu, Qinghua Hu, Xinwang Liu, Pengfei Zhu
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,690 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,386 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |