high-dimensional-data

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

Vald. A Highly Scalable Distributed Vector Search Engine

Fast Best-Subset Selection Library

A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.

A Framework for Dimensionality Reduction in R

High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python.

Poisson pseudo-likelihood regression with multiple levels of fixed effects

A Toolkit for Interactive Statistical Data Visualization

Implementation of NEWMA: a new method for scalable model-free online change-point detection

Deep distance-based outlier detection published in KDD18: Learning representations specifically for distance-based outlier detection. Few-shot outlier detection

A Python package for hubness analysis and high-dimensional data mining

Benchmarking and Visualization Toolkit for Penalized Cox Models

An interactive 3D web viewer of up to million points on one screen that represent data. Provides interaction for viewing high-dimensional data that has been previously embedded in 3D or 2D. Based on graphosaurus.js and three.js. For a Linux release of a complete embedding+visualization pipeline please visit https://github.com/sonjageorgievska/Embed-Dive.

Statistical quality evaluation of dimensionality reduction algorithms

Hubness analysis and removal functions

CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations

Statistics for high-dimensional data (homogeneity, sphericity, independence, spherical uniformity)

The DPA package is the scikit-learn compatible implementation of the Density Peaks Advanced clustering algorithm. The algorithm provides robust and visual information about the clusters, their statistical reliability and their hierarchical organization.