An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R
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Latest commit 17bc89d Dec 12, 2016 @elbamos committed on GitHub Update largeVis.Rd

README.md

largeVis

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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

MNIST

Wiki Words

Clustering With HDBSCAN

Visualize Embeddings

Visualize Embeddings

Building Notes

  • The CRAN binaries are likely to have been compiled with 32-bit ARMA objects, and without OpenMP on OS X.
  • To get 64-bit ARMA objects, add to ~.R/Makevars:

    R_XTRA_CXXFLAGS = -DARMA_64BIT_WORD
    

    and recompile from source. (devtools::install_github("elbamos/largeVis") will work).

  • Getting OpenMP support on OS X is a bit tricky. What I've done, is to install llvm (version 3.8 or later), and then add the following to ~.R/Makevars:

    SHLIB_OPENMP_CFLAGS = -fopenmp
    R_XTRA_CXXFLAGS = -DARMA_64BIT_WORD
    LDFLAGS =  ""-L/usr/local/opt/llvm/lib -Wl,-rpath,/usr/local/opt/llvm/lib"
    CPPFLAGS =  -I/usr/local/opt/llvm/include
    PATH = /usr/local/opt/llvm/bin:$PATH 
    
  • Recompile from source, as above.