Agreement Analysis of Quality Measures for Dimensionality Reduction

Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


High-dimensional data sets commonly occur in various application domains. They are often analysed using dimensionality reduction methods, such as principal component analysis or multidimensional scaling. To determine the reliability of a particular embedding of a data set, users need to analyse its quality. For this purpose, the literature knows numerous quality measures. Most of these measures concentrate on a single aspect, such as the preservation of relative distances, while others aim to balance multiple aspects, such as intrusions and extrusions in k-neighbourhoods. Faced with multiple quality measures with different ranges and different value distributions, it is challenging to decide which aspects of a data set are preserved best by an embedding. We propose an algorithm based on persistent homology that permits the comparative analysis of different quality measures on a given embedding, regardless of their ranges. Our method ranks quality measures and provides local feedback about which aspects of a data set are preserved by an embedding in certain areas. We demonstrate the use of our technique by analysing quality measures on different embeddings of synthetic and real-world data sets.


Scalar Field Quality Measure Jaccard Index Dimensionality Reduction Method Handwritten Digit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.IWRHeidelberg UniversityHeidelbergGermany

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