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.
Original language | English |
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Title of host publication | European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) |
Place of Publication | Bruges (Belgium) |
Publisher | European Symposium on Artificial Neural Networks |
Pages | 635-640 |
Number of pages | 6 |
ISBN (Print) | 9782875870278 |
Publication status | Published - 25 Jul 2016 |