t-distributed stochastic neighbor embedding, abbreviated as t-SNE, provides the novel method to apply non-linear dimensionality reduction technique that preserves the local structure of original dataset. However, in order to transform newly prepared points, a model must be re-trained with whole dataset. This would be extremely inefficient provided that our previous dataset describes the plausible distribution already. Parametric t-SNE instead gives you an explicit mapping between original data and the embedded points. It is achieved by building a parametric model for prediction and training it using the same loss as t-SNE.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
This program was tested under Python 3.6. All necessary packages are contained inside requirements.txt.
After cloning this repository, install required packages by running the following:
pip3 install -r requirements.txt
parametric_tsne.py can be run directly from command-line. See help for details.
python3 parametric_tsne.py -h
Simply create ParametricTSNE instance. The interface was designed similarly to that of scikit-learn estimators.
from parametric_tsne import ParametricTSNE
transformer = ParametricTSNE()
# suppose you have the dataset X
X_new = transformer.fit_transform(X)
# transform new dataset X2 with pre-trained model
X2_new = transformer.transform(X2)-
scikit-learn - Extensive machine learning framework
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Keras - Deep learning framework wrapper that supports TensorFlow, Theano, and CNTK
- Luke Lee - Research and implementation - luke0201
- This project was forked from zaburo-ch's implementation.