NeRV: Neighbor Retrieval Visualizer (NeRV)

In Mthrun/ProjectionBasedClustering: Projection Based Clustering

Neighbor Retrieval Visualizer (NeRV)

Description

Projection is done by the neighbor retrieval visualizer (NeRV)

Usage

NeRV(Data, lambda = 0.1, neighbors = 20, iterations = 10, 

cg_steps = 2, cg_steps_final = 40, randominit = T, OutputDimension = 2,

PlotIt = FALSE, Cls)

Arguments

Data	Numerical matrix of the Data to be projected, [1:n,1:d], nonsymmetric, and consists of n cases of d-dimensional data points with every case having d attributes, variables or features
lambda	Optional: Controls the trustworthiness-continuity tradeoff. Default = 0.1
neighbors	Optional: Set the number of nearest neighbours that each point should have. Should be positive. Default = 20
iterations	Optional: The number of iterations to perform. Default = 10
cg_steps	Optional: The number of conjugate gradient steps to perform per iteration in NeRV's optimization scheme. Default = 2
cg_steps_final	Optional: The number of conjugate gradient steps to perform on the final iteration in NeRV's optimization scheme. Default = 40
randominit	Optional: TRUE: Random Initialization (default), FALSE: PCA initializiation
OutputDimension	Optional: Number of dimensions in the Outputspace, default=2
PlotIt	Optional: Should the projected points be plotted? Default: FALSE. Note: this is only usefull if OutputDimension = 2.
Cls	Optional: Vector containing the number of the class for each row in Data. This is only used to color the points according to their classes if PlotIt = T

Details

Uses the NeRV projection with matrix Data and lambda. Lambda controls the trustworthiness-continuity tradeoff.

An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi: 10.1007/978-3-658-20540-9).

Value

OutputDimension-dimensional matrix of projected points

Note

PCA initialization changes form the original C++ Sourcecode of https://research.cs.aalto.fi/pml/software/dredviz/ to the R version of the projections package. Other changes are made only regarding data types of Rcpp in comparison to the original C++ Source code.

You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.

Author(s)

Michael Thrun, Felix Pape

References

Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.

Jarkko Venna and Samuel Kaski. Nonlinear Dimensionality Reduction as Information Retrieval. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on Artificial Intelligence and Statistics. Omnipress, 2007. JMLR Workshop and Conference Proceedings, Volume 2: AISTATS 2007.

Examples

data('Hepta')
Data=Hepta$Data
## Not run: 
Proj=NeRV(Data)
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)

## End(Not run)

Mthrun/ProjectionBasedClustering documentation built on June 12, 2022, 1:12 p.m.