Nonlinear dimension reduction (NLDR) techniques provide a
low-dimensional representation of high-dimensional data by applying a
non-linear transformation. The complexity of the transformations and
data structure can create wildly different representations depending on
the method and parameter choices. It is difficult to determine whether
any are accurate, which is best, or whether they have missed structure.
To help assess the NLDR and decide on which, if any, is best, we have
developed an algorithm to create a model that is then used to display as
a wireframe in high dimensions. The quollr package provides functions
to implement the algorithm.
You can install the released version of quollr from
CRAN with:
install.packages("quollr") The development version from GitHub with:
install.packages("remotes")
remotes::install_github("JayaniLakshika/quollr")Our approach involves dividing the high-dimensional data set into two parts: a training set to construct the model and a test set to generate model summaries. To implement our approach, first we use a 2D embedding data set as the initial point. The output of our algorithm is a tour that displays the model overlaid on high-dimensional data. Our algorithm comprises two main phases:(1) generate the model in the 2D space, and (2) generate the model in the high-dimensional space. This methodology is available in this package.
questioning how a high-dimensional object looks in low-dimensions using r
- To learn more about the data preprocessing: 1. Data preprocessing
- To learn more about the main steps of the algorithm: 2. Algorithm for visualising the model overlaid on high-dimensional data
- To create the full hexagonal grid: 3. Algorithm for binning data
- To generate model summaries: 4. Generating model summaries
- Quick start guide: 5. Quick start
This package is licensed under the MIT license.