Informative data projections: a framework and two examples

Raul Santos-Rodriguez, Tijl De Bie, Jefrey Lijffijt, Bo Kang

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

8 Citations (Scopus)
80 Downloads (Pure)


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 languageEnglish
Title of host publicationEuropean Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Place of PublicationBruges (Belgium)
PublisherEuropean Symposium on Artificial Neural Networks
Number of pages6
ISBN (Print)9782875870278
Publication statusPublished - 25 Jul 2016


Dive into the research topics of 'Informative data projections: a framework and two examples'. Together they form a unique fingerprint.

Cite this