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KERNEL-BASED METHODS FOR VECTOR-VALUED DATA WITH CORRELATED COMPONENTS
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DOI：10.1137/090758076.png)
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Abstract
En 中文
This paper concerns kernel-based interpolation methods for vector data with correlated components. It gives conditions for a matrix kernel to be conditionally positive definite in an appropriate sense. The conditions allow construction of matrix kernels from nonsymmetric mixtures and scalings of scalar kernels. In particular the kernel used to model the influence of component i on component j can be different from that used to model the influence of component j on component i. The vector modeling techniques considered are particularly appropriate when there are relatively few measurements of one quantity and relatively many of another correlated quantity. The paper concludes with some numerical tests on model problems.
Keywords:
kernel-based methods
matrix conditionally positive definite
correlated components
interpolation
radial basis functions
machine learning
geostatistics
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