@inproceedings{2009-C-MetricLearning-ACM
, author={Frank Nielsen and Aurelien Serandour}
, title={Accuracy of distance metric learning algorithms}
, booktitle={Workshop on Data Mining using Matrices and Tensors (DMMT)}
, month={June}
, year={2009}
, address={Paris, France}
, publisher={ACM}
, doi={10.1145/1581114.1581115}
, abstract={
  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.
  }
}