Improved data visualisation through multiple dissimilarity modelling

Rice, Iain (2016). Improved data visualisation through multiple dissimilarity modelling. Information Sciences, 370-37 , pp. 288-302.

Abstract

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

Publication DOI: https://doi.org/10.1016/j.ins.2016.07.073
Divisions: Engineering & Applied Sciences > Mathematics
Uncontrolled Keywords: Dissimilarity,Euclidean,Multidimensional scaling,Visualisation,Software,Control and Systems Engineering,Theoretical Computer Science,Computer Science Applications,Information Systems and Management,Artificial Intelligence
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2016-11-20
Authors: Rice, Iain

Export / Share Citation


Statistics

Additional statistics for this record