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Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement
[article]
2020
arXiv
pre-print
Distance metric learning can be viewed as one of the fundamental interests in pattern recognition and machine learning, which plays a pivotal role in the performance of many learning methods. One of the effective methods in learning such a metric is to learn it from a set of labeled training samples. The issue of data imbalance is the most important challenge of recent methods. This research tries not only to preserve the local structures but also covers the issue of imbalanced datasets. To do
arXiv:1902.03453v2
fatcat:blajv5catra4rbsruvabmjoaxa