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Multiple-biomarker tensors can be used in supervised classification models as well as in unsupervised models.
The signature kernel itself is used in a tensor product to define the signature product kernel between pairs of proteins.
Both the preaveraged Oseen tensor and the Rotne Prager tensor are used in the study.
A diffusion kurtosis (DK) tensor is a fourth order three-dimensional fully symmetric tensor, which is used in diffusion kurtosis imaging (DKI), a new model in medical engineering.
Actually, it is the renormalized stress-energy tensor that is used in calculating backreaction effects; see below.
The spoligotype deletions found informative by the feature selection algorithm are used in the generation of multiple-biomarker tensors.
Different forms are used in different ways.
The tensor polynomial form of the maximum stress criterion is used in conjunction with a progressive failure simulation methodology.
Once the main interactions were determined for each of the sites, full tensor product smooths (function 't2' and 'te') were used in the final models.
Moreover, in telecommunication applications, the input tensor can be used as a code tensor.
In contrast to linear subspace-learning methods (PCA) in which all of the features should be transformed as a vector, multi-linear subspace-learning method (GND-ICA) can be used to process input data in a tensor form.
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