Improved data visualisation through multiple dissimilarity modelling

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• 作者：Iain Rice ^{i.rice@aston.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Dissimilarity ; Visualisation ; Multidimensional scaling ; Euclidean
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：20 November 2016
• 年：2016
• 卷：370-371
• 期：Complete
• 页码：288-302
• 全文大小：5255 K

文摘

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.