Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/e19120659 | ENTROPY |
Keywords | Field | DocType |
quantum formalism applications,minimum distance classification,rescaling parameter | Quantum,Mathematical optimization,Quantum machine learning,Quantum algorithm,Operator (computer programming),Trace distance,Classifier (linguistics),Mathematics,Encoding (memory),Free parameter | Journal |
Volume | Issue | Citations |
19 | 12 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrica Santucci | 1 | 2 | 1.06 |