NDLI: Similarity Learning in Nearest Neighbor and Relief Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Qamar, A.M. Gaussier, E.
Copyright Year	2010
Abstract	In this paper, we study the links between RELIEF, a well-known feature re-weighting algorithm and SiLA, a similarity learning algorithm. On one hand, SiLA is interested in directly reducing the leave-one-out error or 0-1 loss by reducing the number of mistakes on unseen examples. On the other hand, it has been shown that RELIEF could be seen as a distance learning algorithm in which a linear utility function with maximum margin was optimized. We first propose here a version of this algorithm for similarity learning, called RBS (for RELIEF-Based Similarity learning). As RELIEF, and unlike SiLA, RBS does not try to optimize the leave-one-out error or 0-1 loss, and does not perform very well in practice, as we illustrate on two UCI collections. We thus introduce a stricter version of RBS, called sRBS, aiming at relying on a cost function closer to the 0-1 loss. Experiments conducted on several datasets illustrate the different behaviors of these algorithms for learning similarities for kNN classification. The results indicate in particular that the 0-1 loss is a more appropriate cost function than the one implicitly used by RELIEF.
Starting Page	183
Ending Page	189
File Size	253016
Page Count	7
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Machine learning algorithms Symmetric matrices Support vector machine classification Cost function SiLA algorithm RELIEF algorithm similarity learning machine learning Equations Nearest neighbor searches kNN classification
Content Type	Text
Resource Type	Article

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