Projects sharing features with Prince

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Parallel t-SNE implementation with Python and Torch wrappers.

A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.

Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,

This project is a manifold learning and non-linear dimensionality reduction library used to project high-dimensional data into lower-dimensional spaces while preserving topological structure. It functions as a parametric embedding framework and a topological data visualization library for identifying clusters and patterns within complex datasets. The library distinguishes itself through parametric neural mapping, which uses neural networks to learn functional mappings that allow for out-of-sample projections and the reconstruction of original data. It supports supervised and semi-supervised d

Published in the 45th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020. DOI (ICASSP Publication): https://doi.org/10.1109/ICASSP40776.2020.9053924 DOI (Code Ocean): https://doi.org/10.24433/CO.7476989.v1

imbalanced-learn is a dataset balancing framework and Python machine learning extension designed to resample training data and reduce the impact of class imbalance. It provides a toolkit of algorithms for adjusting class distributions to improve model performance on minority class prediction. As a scikit-learn resampling library, it extends the ecosystem with specialized tools for balancing datasets through over-sampling and under-sampling techniques. This allows for the correction of skewed class proportions to reduce model bias toward the majority class. The library implements the scikit-l

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Feature selector is a tool for dimensionality reduction of machine learning datasets.