NDLI: Learning a Combination of Dissimilarities from a Set of Equivalence Constraints

Content Provider	IEEE Xplore Digital Library
Author	Martín-Merino, M.
Copyright Year	2010
Abstract	Applications have emerged in the last years in which several dissimilarities and data sources provide complementary information about the problem. Therefore, metric learning algorithms should be developed that integrate all this information in order to reflect better which is similar for the user and the problem at hand. In this paper, we propose a semi-supervised algorithm to learn a linear combination of dissimilarities using the a priori knowledge provided by human experts. A priori knowledge is formulated in the form of equivalence constraints. The minimization of the error function is based on a quadratic optimization algorithm. A $L_2$ norm regularizer is included that penalizes the complexity of the family of distances and avoids over fitting. The experimental results suggest that the method proposed outperforms a standard metric learning algorithm and improves classification and clustering results based on a single dissimilarity.
Starting Page	41
Ending Page	48
File Size	313759
Page Count	8
File Format	PDF
ISBN	9781424492442
DOI	10.1109/ICDMW.2010.130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Algorithm design and analysis Pattern Recognition Neodymium Clustering algorithms Kernel Bioinformatics Machine Learning Optimization
Content Type	Text
Resource Type	Article

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