Manifold learning in R

Installation

To install the package, run the following codes in R:

BiocManager::install("RDRToolbox")

# Without vignettes
devtools::install_github("kcf-jackson/maniTools")

# With vignettes (Note: it will take some time to build all the vignettes)
install.packages(c("knitr", "fields", "purrr", "animation", "R.matlab", "pixmap")
devtools::install_github("kcf-jackson/maniTools", build_vignettes = TRUE)
browseVignettes("maniTools")

Pre-built vignettes

1. A list of supported dimension-reduction techniques
2. Face ordering (Wang, 2011))
3. Face orientation (Tenenbaum, Silva and Langford, 2000)
4. Robot Wifi trajectory (Lawrence, 2012)
5. Shiny App

References:

1. Wang, J. (2011). Geometric structure of high-dimensional data and dimensionality reduction (pp. 294). Springer Berlin Heidelberg.
2. Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. science, 290(5500), 2319-2323.
3. Lawrence, N. D. (2012). A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models. The Journal of Machine Learning Research, 13(1), 1609-1638.