Compressive Mahalanobis Metric Learning Adapts to Intrinsic Dimension

arXiv:2309.05751v3 Announce Type: replace
Abstract: Metric learning aims at finding a suitable distance metric over the input space, to improve the performance of distance-based learning algorithms. In high-dimensional settings, it can also serve as dimensionality reduction by imposing a low-rank restriction to the learnt metric. In this paper, we consider the problem of learning a Mahalanobis metric, and instead of training a low-rank metric on high-dimensional data, we use a randomly compressed version of the data to train a full-rank …

abstract algorithms arxiv cs.lg dimensionality intrinsic low paper performance serve space stat.ml type