PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Graph Laplacians for Semi--Supervised Learning
Andreas Argyriou, Mark Herbster and Massimiliano Pontil
In: 19th Conference on Neural Information Processing Systems, 5-8 December 2005, Vancouver, Canada.


A foundational problem in semi-supervised learning is the construction of a graph underlying the data. We propose to use a method which optimally combines 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 different 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:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:1217
Deposited By:Andreas Argyriou
Deposited On:27 November 2005