No fuss metric learning, a Hilbert space scenario
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]
|Collections||ANU Research Publications|
|Source:||Pattern Recognition Letters|
|1-s2.0-S0167865517303124-main.pdf||1.99 MB||Adobe PDF||Request a copy|
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