KLMeasure {ProjectionBasedClustering}

Smoothed Precision and Recall

Description

Computes the quality measurement of rank-based smoothed precision an recall, with cost function based on Kullback-Leibler-divergence (see [Venna2010]) used to evaluated dimensionality reduction methods.

Usage

KLMeasure(Data, pData, NeighborhoodSize = 20L)

Arguments

Data

numerical matrix of data: n cases in rows, d variables in columns

pData

numerical matrix of projected data: n cases in rows, k variables in columns, where k is the projection output dimension

NeighborhoodSize

Number of points in neighborhood to be considered. Default is 20

Details

This is a wrapper that is used in the DRquality to investigate varius quality measurements [Thrun et al, 2023]. The paper indicates, that the Gabriel classification error seems to be a good alternative. [Thrun et al, 2023].

Value

SmoothedPrecision

Scalar, smoothed precision value

SmoothedRecall

Scalar, smoothed recall value

Note

C++ source code comes from https://research.cs.aalto.fi/pml/software/dredviz/

Author(s)

Luca Brinkmann, Felix Pape

References

[Venna2010]: 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.

[Thrun et al, 2023] Thrun, M.C, Märte, J., Stier, Q.: Analyzing Quality Measurements for Dimensionality Reduction, Machine Learning and Knowledge Extraction (MAKE), Vol 5., accepted, 2023.

See Also

An alternative measure is the ContTrustMeasure, see also GabrielClassificationError

Examples

data('Hepta')
Data=Hepta$Data
res=MDS(Data)
Proj = res$ProjectedPoints

kl_m = KLMeasure(Hepta$Data, Proj)
# Smoothed precision
print(kl_m[[1]])
# Smoothed recall
print(kl_m[[2]])