Name
Abstract | ||
|---|---|---|
Semi-supervised support vector machine (S3VM) is a popular strategy for many machine learning tasks due to the expensiveness of getting enough labeled data. In this paper, we propose a quantum Help-Training S3VM and design a quantum Parzen window model to select n1+n2 unlabeled data from l labeled and n unlabeled data set in each iteration, the time complexity is O(tau <mml:msqrt>nn1</mml:msqrt>+tau <mml:msqrt>nn2</mml:msqrt>+tau <mml:msqrt>n</mml:msqrt>) for tau iterations, which exhibits a quadratic speed-up over classical algorithm, we adopt quantum linear system to build Lagrangian multipliers with accuracy epsilon, the time complexity is O(tau kappa 3 epsilon -3polylog(N(n+l))), where condition number is kappa and feature dimension is N, it is exponentially faster than classical S3VM algorithm. Our scheme has two significant merits, (i) we provide the first quantum method for semi-supervised learning, which uses multiple unlabeled data with quantum superposition to predict Lagrangian multipliers at the same time, (ii) quantum matrix decomposition method avoids building matrices of different dimensions in one iteration; specially, this work provides inspiration to explore the potential quantum machine learning applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11128-020-02770-x | QUANTUM INFORMATION PROCESSING |
Keywords | DocType | Volume |
Semi-supervised support vector machine (<mml:msup>S<mml:mn>3</mml:mn></mml:msup>VM,Lagrangian multipliers,Quantum Parzen window,Controlled swap test operation | Journal | 19 |
Issue | ISSN | Citations |
9 | 1570-0755 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Yan Hou | 1 | 2 | 2.47 |
| Li Jian | 2 | 85 | 31.63 |
| Xiu-Bo Chen | 3 | 163 | 36.11 |
| Hengji Li | 4 | 1 | 4.81 |
| Chaoyang Li | 5 | 3 | 4.83 |
| Yuan Tian | 6 | 6 | 8.99 |
| Leilei Li | 7 | 0 | 2.37 |
| Zhengwen Cao | 8 | 0 | 0.68 |
| Na Wang | 9 | 2 | 1.71 |