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.
|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|
|Number of pages||6|
|Publication status||Published - 25 Jul 2016|
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) (pp. 635-640). European Symposium on Artificial Neural Networks. https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2016