We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.

A quantum-inspired version of the classification problem

SERGIOLI, GIUSEPPE;SANTUCCI, ENRICA;GIUNTINI, ROBERTO
2017-01-01

Abstract

We address the problem of binary classification by using a quantum version of the Nearest Mean Classifier (NMC). Our proposal is indeed an advanced version of previous one (see Sergioli et al. 2017 that i) is able to be naturally generalized to arbitrary number of features and ii) exhibits better performances with respect to the classical NMC for several datasets. Further, we show that the quantum version of NMC is not invariant under rescaling. This allows us to introduce a free parameter, i.e. the rescaling factor, that could be useful to get a further improvement of the classification performance.
2017
Density operators; Nearest mean classifier; Rescaling invariance; Mathematics (all); Physics and Astronomy (miscellaneous)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/214009
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