UniformManifoldApproximationProjection: Uniform Manifold Approximation and Projection

In ProjectionBasedClustering: Projection Based Clustering

Uniform Manifold Approximation and Projection

Description

Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by [McInnes et al., 2018].

Usage

UniformManifoldApproximationProjection(DataOrDistances, k,

Epochs,OutputDimension=2,Algorithm='umap_pkg',PlotIt=FALSE,Cls,...)

Arguments

DataOrDistances	Numerical matrix defined as either Data, i.e., [1:n,1:d], nonsymmetric, and consists of n cases of d-dimensional data points with every case having d attributes, variables or features, or Distances, i.e.,[1:n,1:n], symmetric and consists of n cases, e.g., as.matrix(dist(Data,method))
k	number of k nearest neighbors, Important parameter, if not given, settings of package umap will be used, default of package umap is currently 15
Epochs	Number of eppochs (scalar), i.e, training length, default of package umap is currently 200
OutputDimension	Number of dimensions in the Outputspace, default=2
Algorithm	"umap_pkg": provides an interface for two implementations. One is written from scratch other one requires python umap "uwot_pkg": complete re-implementation in R (and C++, via the 'Rcpp' package) of uwot
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
Cls	Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.
...	one of the other 21 parameters that can be specified, please see umap.defaults of package umap for details or parameters to be set in package uwot depending on the choice of Algorithm.

Details

To the knowledge of the author of this function no peer-reviewed publication of the method exists. Use with greate care.

Value

List of

ProjectedPoints	[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection
ModelObject	output of umap or of package uwot depending on Algorithm
Setting	specific settings used in UniformManifoldApproximationProjection

Note

Uniform Manifold Approximation and Projection and U-matrix [Ultsch/Siemon, 1990] are both sometimes abbreviated with Umap. Hence the abbreveviation is omitted here.

Author(s)

Michael Thrun

References

[McInnes et al., 2018] McInnes, L., Healy, J., & Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction, arXiv preprint arXiv:1802.03426, 2018.

[Ultsch/Siemon, 1990] Ultsch, A., & Siemon, H. P.: Kohonen's Self Organizing Feature Maps for Exploratory Data Analysis, International Neural Network Conference, pp. 305-308, Kluwer Academic Press, Paris, France, 1990.

See Also

umap of umap

umap of uwot

Examples

data('Hepta')
Data=Hepta$Data

Proj=UniformManifoldApproximationProjection(Data)

## Not run: 
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)

## End(Not run)

ProjectionBasedClustering documentation built on Oct. 12, 2023, 1:07 a.m.