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No fuss metric learning, a Hilbert space scenario

Faraki, Masoud; Harandi, Mehrtash; Porikli, Fatih


In this paper, we devise a kernel version of the recently introduced keep it simple and straightforward metric learning method, hence adding a novel dimension to its applicability in scenarios where input data is non-linearly distributed. To this end, we make use of the infinite dimensional covariance matrices and show how a matrix in a reproducing kernel Hilbert space can be projected onto the positive cone efficiently. In particular, we propose two techniques towards projecting on the...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Journal article
Source: Pattern Recognition Letters
DOI: 10.1016/j.patrec.2017.09.017
Access Rights: Open Access


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