Online metric learning methods using soft margins and least squares formulations

Abstract

Online metric learning using margin maximization has been introduced as a way to learn appropriate dissimilarity measures in an efficient way when information as pairs of examples is given to the learning system in a progressive way. These schemes have several practical advantages with regard to global ones in which a training set needs to be processed. On the other hand, they may suffer from a poor performance depending on the quality of the examples and the particular tuning or other implementation details. This paper formulates several online metric learning alternatives using a passive-aggressive schema. A new formulation of the online problem using least squares is also introduced. The relative behavior of the different alternatives is studied and comparative experimentation is carried out to put forward the benefits and weaknesses of each alternative. © 2012 Springer-Verlag Berlin Heidelberg.