We propose a quantum version of the well known minimum distance classification model called Nearest Mean Classifier
(NMC). In this regard, we presented our first results in two previous works. First, a quantum counterpart of the NMC for two-dimensional problems was introduced, named Quantum Nearest Mean Classifier
(QNMC), together with a possible generalization to any number of dimensions. Secondly, we studied the n
-dimensional problem into detail and we showed a new encoding for arbitrary n
-feature vectors into density operators. In the present paper, another promising encoding is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows us to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted: (i)
to compare the NMC and QNMC performance on different datasets; and (ii)
to study the effects of the non-invariance under uniform rescaling for the QNMC.
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