PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining graph Laplacians for semi--supervised learning
Andreas Argyriou, Mark Herbster and Massimiliano Pontil
In: NIPS 2005, December, 2005, Vancouver, CA.


We build on recent methods for graph regularization in semi-supervised learning. A foundational problem for these methods is the construction of the underlying graph. We propose to use a method which can optimally combine a number of differently constructed graphs. For each of these graphs we associate a basic graph kernel. We then compute an optimal {\em combined} kernel. This kernel solves an extended regularization problem which requires a joint minimization over both the data and the set of graph kernels. We present encouraging results on a variety of OCR tasks where the optimal combined kernel is computed from graphs constructed with a variety of distance functions and the `$k$' in nearest neighbors.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2720
Deposited By:Massimiliano Pontil
Deposited On:22 November 2006