Topics

High dimensional data analysis covers a wide spectrum of topics. Subsequently we list a non-complete list of topics which are interesting to HDDA. For many of the topics you can find a collaboration partner or even expert in the member section.

Topics

• Systematic studies of how the curse of dimensionality affects data mining methods
• Models of low intrinsic dimension: sparse representation, manifold models, latent structure models, large margin, other?
• How to exploit intrinsic dimension in optimisation tasks for data mining?
• New data mining techniques that scale with the intrinsic dimension, or exploit some properties of high dimensional data spaces
• Dimensionality reduction
• Methods of random projections, compressed sensing, and random matrix theory applied to high dimensional data mining and high dimensional optimisation
• Theoretical underpinning of mining data whose dimensionality is larger than the sample size
• Classification, regression, clustering, visualisation of high dimensional complex data sets
• Functional data mining
• Data presentation and visualisation methods for very high dimensional data sets
• Data mining applications to real problems in science, engineering or businesses where the data is high dimensional