Laplacian Regularized Kernel Canonical Correlation Ensemble for Remote Sensing Image Classification
IEEE Geoscience and Remote Sensing Letters(2019)
摘要
Kernel canonical correlation analysis (KCCA) is an efficient dimensionality reduction tool in the application of remote sensing image classification. However, it suffers from the problem of parametric sensitivity since a single kernel is used. In this letter, a KCCA ensemble framework is put forward to improve the robustness of KCCA. Following the philosophy that two heads are better than one, mul...
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关键词
Kernel,Correlation,Remote sensing,Laplace equations,Robustness,Feature extraction,Computational complexity
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