Kernel machines

UiT Machine Learning Group advances kernel machines


Our group has over several years had a strong focus on innovating kernel machines, often in a synergistic fashion with information theoretic learning. UiT Machine Learning Group developed methods such as kernel entropy component analysis and the information cut. In recent years, the group has investigated the concept of probabilistic cluster kernels.

The kernel team consists primarily of

  • Sigurd Løkse
  • Filippo Bianchi
  • Cristina Soguero Ruiz
  • Karl Øyvind Mikalsen
  • Robert Jenssen

Representative papers:

Optimized Kernel Entropy Components

E. Izquierdo-Verdiguier, V. Lappara, R. Jenssen, L. Gomez-Chova and G. Camps-Valls, IEEE Transactions on Neural Networks and Learning Systems, 2016.

Spectral Clustering with the Probabilistic Cluster Kernel

E. Izquierdo-Verdiguier, R. Jenssen, L. Gomez-Chova and G. Camps-Valls, Neurocomputing, 2015.

Kernel Covariance Series Smoothing

C. Sogureo-Ruiz and R. Jenssen, IEEE MLSP, 2015.


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