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

a predictive model to determine the income level for people in US. Imputed and manipulated large and high dimensional data using data.table in R. Performed SMOTE as the dataset is highly imbalanced. Developed naïve Bayes, XGBoost and SVM models for classification

Example of computing n-dimensional hyperspheres by Monte Carlo methods, both threaded and non-threaded

Hypersphere volume computation for low dimensionality using Monte Carlo techniques and threaded Ruby

Predicting the winners of Miss Finland 2016 with penalized logistic regression, using image data.

A python toolbox for visualizing and manipulating high-dimensional data

Constellat.io

Feature Selection by Optimized LASSO algorithm

An R package for testing high-dimensional covariance matrices

High-Dimensional Regularized Discriminant Analysis

MinMax Circular Sector Arc for External Plagiarism’s Heuristic Retrieval Stage code

MWPCR-with-Matlab

An advanced version of K-Means using Particle swarm optimization for clustering of high dimensional data sets, which converges faster to the optimal solution.

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

jQuery plugin to easily browse and highlight your JSON

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/Em…

Gene network reconstruction using global-local shrinkage priors