Novel Kernel Orthogonal Partial Least Squares for Dominant Sensor Data Extraction
Orthogonal Partial Least Squares (OPLS) methods are aimed at finding the dominant factors from predictor variables that can maximize cross-covariance between the factors themselves and response variables while a high correlation between them should also be satisfied at the same time. Compared with d...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9001014/ |