An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R

Last update: May 25, 2022

Related tags

Data Analysis largeVis

Overview

largeVis

This is an implementation of the largeVis algorithm described in (https://arxiv.org/abs/1602.00370). It also incorporates:

A very fast algorithm for estimating k-nearest neighbors, implemented in C++ with Rcpp and OpenMP. See the Benchmarks file for performance details.
Efficient implementations of the clustering algorithms:
- HDBSCAN
- OPTICS
- DBSCAN
Functions for visualizing manifolds like this.

News Highlights

Version 0.1.10 re-adds clustering, and also adds momentum training to largeVis, as well as a host of other features and improvements.
Version 0.1.9.1 has been accepted by CRAN. Much grattitude to Uwe Ligges and Kurt Hornik for their assistance, advice, and patience.

Some Examples

Clustering With HDBSCAN

Visualize Embeddings

Building Notes

Note on R 3.4: Before R 3.4, the CRAN binaries were likely to have been compiled without OpenMP, and getting OpenMP to work on Mac OS X was somewhat tricky. This should all have changed (for the better) with R 3.4, which natively using clang 4.0 by default. Since R 3.4 is new, I'm not able to provide advice, but am interested in hearing of any issues and any workarounds to issues that you may discover.

Owner

GitHub Repository

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