We apply a recent formalization of visualization as information retrieval to linear projections. We introduce a method that optimizes a linear projection for an information retrieval task: retrieving neighbors of input samples based on their low-dimensional visualization coordinates only. The simple linear projection makes the method easy to interpret, while the visualization task is made well-defined by the novel information retrieval criterion. The method has a further advantage: it projects input features, but the input neighborhoods it preserves can be given separately from the input features, e.g. by external data of sample similarities. Thus the visualization can reveal the relationship between data features and complicated data similarities. We further extend the method to kernel-based projections. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Peltonen, J. (2009). Visualization by linear projections as information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5629 LNCS, pp. 237–245). https://doi.org/10.1007/978-3-642-02397-2_27
Mendeley helps you to discover research relevant for your work.