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Title: Learning from general label constraints
Authors: De Bie, Tijl
Suykens, Johan
De Moor, Bart #
Issue Date: 2004
Publisher: Springer-verlag berlin
Host Document: Proc. of the joint IAPR international workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition (SSSPR 2004) vol:3138 pages:671-679
Conference: joint IAPR international workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition (SSSPR 2004) location:Lisbon, Portugal date:Aug. 2004
Abstract: Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and in vision however show that this setting often is an oversimplification of reality. While label information is of course invaluable in most cases, it would be a huge waste to ignore the information on the cluster structure that is present in an (often much larger) unlabeled sample set. Several recent contributions deal with this topic: given partially labeled data, exploit all information available. In this paper, we present an elegant and efficient algorithm that allows to deal with very general types of label constraints in class learning problems. The approach is based on spectral clustering, and leads to an efficient algorithm based on the simple eigenvalue problem.
Description: \emph{Proc. of the joint IAPR international workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition (SSSPR 2004)}, Lisbon, Portugal, Aug. 2004
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Electrical Engineering - miscellaneous
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
# (joint) last author

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