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Non-linear Metric Learning
On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of χ2-LMNN, obtain best results in 19 out of 20 learning settings.
The variational hierarchical EM algorithm for clustering hidden Markov models
In this paper, we derive a novel algorithm to cluster hidden Markov models (HMMs) according to their probability distributions.
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint
The goal of this paper is to investigate the advantages and disadvantages of learning in Banach spaces over Hilbert spaces.
On the Convergence of the Concave-Convex Procedure
In this paper, we follow a different reasoning and show how Zangwills global convergence theory of iterative algorithms provides a natural framework to prove the convergence of CCCP, allowing a more elegant and simple proof.
