.png){kind=icon}
Return
Quantum Isomap algorithm for manifold learning
.png){kind=icon}
PRE
AI
DOI：10.1103/PhysRevApplied.22.014049.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Isometric feature mapping (Isomap) is a nonlinear dimensionality reduction algorithm based on the concept of manifold learning. The algorithm is widely used in neuroimaging, spectral analysis, and other fields. While Isomap performs well in certain scenarios, it still faces the challenge of time complexity when dealing with large-scale data sets. In this paper, we proposed a quantum Isomap algorithm that runs with quadratic dependence on the number of data points. Technically, our algorithm is based on two subroutines, quantum Floyd's algorithm and quantum multidimensional scaling. They are of independent interest and may serve as potential tools for addressing other problems.
Journal
IF:
4.4
Papers:
7.1K
Citations:
2.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
View more