Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Metric Learning Tutorial

Version 1 : Received: 7 September 2018 / Approved: 7 September 2018 / Online: 7 September 2018 (11:34:37 CEST)

How to cite: Jain, P. Metric Learning Tutorial. Preprints 2018, 2018090131 (doi: 10.20944/preprints201809.0131.v1). Jain, P. Metric Learning Tutorial. Preprints 2018, 2018090131 (doi: 10.20944/preprints201809.0131.v1).


Most popular machine learning algorithms like k-nearest neighbour, k-means, SVM uses a metric to identify the distance(or similarity) between data instances. It is clear that performances of these algorithm heavily depends on the metric being used. In absence of prior knowledge about data we can only use general purpose metrics like Euclidean distance, Cosine similarity or Manhattan distance etc, but these metric often fail to capture the correct behaviour of data which directly affects the performance of the learning algorithm. Solution to this problem is to tune the metric according to the data and the problem, manually deriving the metric for high dimensional data which is often difficult to even visualize is not only tedious but is extremely difficult. Which leads to put effort on \textit{metric learning} which satisfies the data geometry.Goal of metric learning algorithm is to learn a metric which assigns small distance to similar points and relatively large distance to dissimilar points.

Subject Areas

metric learning; algorithms

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