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The basis of the RBC-space is derived using the singular vectors of a set of known RBCs.
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SVD can be used to reduce the dimensionality of a data set, but our method uses the singular vectors generated by the decomposition to identify patterns that may relate to grain filling in rice.
Only for the special case that the MIMO channel among all these users are orthogonal that the CCI will be zero if we directly use the singular vector of each user as its precoding vector.
Zhang et al. [38] calculated a difference vector between the reference and distorted images using the singular values of wavelet decomposition.
Using the singular value decomposition (SVD), (boldsymbol {DD}^{top } = sum _{k=1}^{K} sigma _{k} boldsymbol {v}_{k} boldsymbol {v}_{k}^{top }), where the singular vectors v k are orthonormal and the singular values σ k >0.
In fact, a classification of genes using the right singular vector with the largest singular value was of low quality.
It may be possible to improve the null density estimate further using the second singular vector in some cases, but we will not attempt this here.
This decomposition considers some correlated information, using the right matrix of the singular vectors.
Therefore, the problem is to obtain the phase set { θ → i [ k ] } and correcting the singular vectors using (9).
In BioDICE, the singular vectors obtained by SVD are used in place of the principal components to imprint the initial SOM lattice with fingerprints of the input objects.
To this end, we must remove the contribution of the singular vectors associated to the largest singular value d 1 using: boldsymbol{K}_{xy} - d_{1}boldsymbol{u}_{1}boldsymbol{v}_{1}^{T} = sumlimits_{i=2}^{N}d_{i}boldsymbol{u}_{i}boldsymbol{v}_{i}^{T}~.
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