Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation
Generating low-rank approximations of kernel matrices that arise in nonlinear machine learning techniques holds the potential to significantly alleviate the memory and computational burdens. A compelling approach centers on finding a concise set of exemplars or landmarks to reduce the number of simi...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9449889/ |