Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
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Updated
Mar 5, 2024 - Python
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
A Python toolbox for gaining geometric insights into high-dimensional data
High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python.
Deep distance-based outlier detection published in KDD18: Learning representations specifically for distance-based outlier detection. Few-shot outlier detection
Implementation of NEWMA: a new method for scalable model-free online change-point detection
A Python package for hubness analysis and high-dimensional data mining
Hubness analysis and removal functions
A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.
python library to perform Locality-Sensitive Hashing for faster nearest neighbors search in high dimensional data
A simple library for t-SNE animation and a zoom-in feature to apply t-SNE in that region
A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets (JASA-20 paper)
Paxplot is a Python visualization library for parallel coordinate plots based on matplotlib.
High Dimensional Regression Coefficient Analysis for Functional Data
Code for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
A small library for regridding Earth system data with vectorized sparse linear transforms
Easy evaluation and plotting of statistical data and high-dimensional distributions in python - Fast generation, loading and storing of custom datasets.
[TMLR] Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python
A pure implementation for sparse denoising autoencoder with adaptive evolutionary training using Scipy. The sparse implementation makes the algorithm scalable to high dimensional data and trainable on CPUs.
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