Name
Abstract | ||
|---|---|---|
Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11128-018-1924-8 | Quantum Information Processing |
Keywords | Field | DocType |
Feature selection,Forward selection,Backward elimination,Quantum machine learning,Grover’s algorithm | Quantum machine learning,Pattern recognition,Feature selection,Inference,Quantum mechanics,Image retrieval,Quantum computer,Preprocessor,Artificial intelligence,Classifier (linguistics),Grover's algorithm,Physics | Journal |
Volume | Issue | ISSN |
17 | 7 | 1570-0755 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhimin He | 1 | 3 | 2.51 |
| Lvzhou Li | 2 | 165 | 15.36 |
| Zhiming Huang | 3 | 15 | 2.53 |
| Haozhen Situ | 4 | 43 | 10.96 |