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Publication - Dr Tijl De Bie

    Informative data projections

    a framework and two examples

    Citation

    Santos-Rodriguez, R, De Bie, T, Lijffijt, J & Kang, B, 2016, ‘Informative data projections: a framework and two examples’. in: European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). European Symposium on Artificial Neural Networks, Bruges (Belgium), pp. 635-640

    Abstract

    Projection Pursuit aims to facilitate visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge in Projection Pursuit is the design of a projection index–a suitable quality measure to maximise. We introduce a strategy for tackling this problem based on quantifying the amount of information a projection conveys, given a user’s prior beliefs about the data. The resulting projection index is a subjective quantity, explicitly dependent on the intended user. As an illustration, we developed this principle for two kinds of prior beliefs; the first leads to PCA, the second leads to a novel projection index, which we call t-PCA, that can be regarded as a robust PCA-variant. We demonstrate t-PCA’s usefulness in comparative experiments against PCA and FastICA, a popular PP method.

    Full details in the University publications repository