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COT [13] represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector.
Currently, the most recent approach in terms of prediction accuracy is COT [13] model, which represented the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector.
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To identify relevant nodes, we introduce a latent vector in the generative model for each network k, which is tightly linked to as explained later.
PCA is a latent vector-based method that compresses data into a few orthogonal vectors, principal components (PCs), summarizing the variation and correlation patterns in the data.
The proposed approach treats the multiple infectious agents as an unobserved or latent vector of variables, the values of which are informed by the pathogen measurements, pneumonia status, and covariate values.
Therefore, we assume that the mean vector of the neutral speaker μ t is determined using a latent phonological vector h as begin{array}{*{20}l} boldsymbol{mu}_{t} = mathbf{W} boldsymbol{h}_{t} + boldsymbol{b}, end{array} (3).
If the first latent vector is obtained, the other latent vectors can be estimated by the deflation of W ˜ and A. Therefore, we firstly find the latent vector t1 and the corresponding loading matrices Q 1 m m = 2, 3 and v1.
In a second-order model, some latent variables are made of multiple dimensions and every single dimension can be measured by indicators such as a latent variable.
Another issue concerning latent variables in ecological research is that some "observable variables" (e.g., salinity, pH, temperature, and density) are measured as a latent variable.
RMA is described more generally as a latent trait analysis.
The classification of interest is regarded as a latent trait.
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