Welcome!
To configure all the hyperparameters of Weighted t-SNE, you only need to create a config.py file. An example can be downloaded here. It also contains the necessary documentation. To set the weights of each features you should use a .csv file as in this example.
You will need Python 3 to run this code. Check if the needed libraries are installed with:
python3 check_dep.py
And for the weighted t-SNE visualization, run:
python3 wtsne.py config.py
You can download the datasets used in the experiments here and the scores from the feature scorers here.
The complete CuMiDa can be found here: https://sbcb.inf.ufrgs.br/cumida
You can download the complete configuration of the experiments here.
Interactive plots of the results can be seen here: https://sbcblab.github.io/wtsne
This implementation of relevance aggregation uses the following Python 3.7 libraries:
If you use our code, methods, or results in your research, please consider citing the main publication of weithed t-SNE:
To be published.
Bibtex entry:
@article{grisci2021relevance,
title={},
author={},
journal={},
year={},
doi = {},
publisher={}
}
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Bruno I. Grisci - PhD candidate (Institute of Informatics - UFRGS)
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Prof. Dr. Mario Inostroza-Ponta - Associate Professor (Departamento de Ingeniería Informática - USACH)
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Prof. Dr. Marcio Dorn - Associate Professor (Institute of Informatics - UFRGS)