accuracy of distance metric learning algorithms
In this paper, we wanted to compare distance metric-learning algorithms on UCI datasets. We wanted to assess the accuracy of these algorithms in many situations, perhaps some that they were not initially designed for. We looked for many algorithms and chose four of them based on our criteria. We also selected six UCI datasets. From the data's labels, we create similarity dataset that will be used to train and test the algorithms. The nature of each dataset is different (size, dimension), and the algorithms' results may vary because of these parameters. We also wanted to have some robust algorithms on dataset whose similarity is not perfect, whose the labels are no well defined. This occurs in multi-labeled datasets or even worse in human-built ones. To simulate this, we injected contradictory data and observed the behavior of the algorithms. This study seeks for a reliable algorithm in such scenarios keeping in mind future uses in recommendation processes.
metric learning mahalanobis distance bregman divergences