Improved data visualisation through multiple dissimilarity modelling

Iain Rice

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)288-302
    Number of pages15
    JournalInformation Sciences
    Early online date4 Aug 2016
    Publication statusPublished - 20 Nov 2016


    • Dissimilarity
    • Euclidean
    • Multidimensional scaling
    • Visualisation


    Dive into the research topics of 'Improved data visualisation through multiple dissimilarity modelling'. Together they form a unique fingerprint.

    Cite this