Multiclass Penalized Likelihood Pattern Classification Algorithm

Publisher:
Springer Berlin Heidelberg
Publication Type:
Journal Article
Citation:
Lecture Notes in Computer Science, 2012, LNCS 7665, 7665 pp. 141 - 148
Issue Date:
2012-01
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Penalized likelihood is a general approach whereby an objective function is defined, consisting of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, this objective function is maximized, yielding a solution that achieves some sort of trade-off between the faithfulness and the smoothness of the fit. In this paper we extend the penalized likelihood classification that we proposed in earlier work to the multi class case. The algorithms are based on using a penalty term based on the K-nearest neighbors and the likelihood of the training patterns classifications. The algorithms are simple to implement, and result in a performance competitive with leading classifiers.
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