The resulting order of vectors changes to [1, 3, 15, 20], [1, 4, 5, 30], [4, 6, 7, 8], which corresponds to the order of features: k 1, k 3, k 2. This provides a global ranking of the three features.
The other advantage is that, since our preference relation induces a finer order on vectors of objective space than that achieved by the Pareto dominance relation, it is appropriate to cope with problems having a high number of objectives.
The tensors, which are higher-order equivalents of vectors (first order) and matrices (second order), are also known as multidimensional arrays.
Then, we also perform the re-ordering procedure to correct the mixed order of basis vectors caused by the initial sorting and obtain a re-ordered reduced matrix H'in,αas follows: H ′ in, α = h ′ α, 1, h ′ α, 2, h ′ α, 3, h ′ α, 4 where h ′ α, θ ( i ) = u i. (20).
Therefore, the order of basis vectors is changed, when swapping event occurs.
Compared to the DVS-I (pLA-1 and pHf1g3T-1), the DVS-II (pLT-2 and pHf1g3A-2) stable stransformationmation efficiency regardless of the order of the vectors introduced into the host cells.
However, RLS-DLA method requires to permute the order of training vectors and adapt the forgetting factor to satisfy the randomness and convergence properties of the online nature of the algorithm.
Let π be the permutation order of basis vectors caused by the swapping event, so that we can express s'π (i)= g i, where s'(i is the reduced basis of s'.
Let θ be the permutation order of basis vectors, then the input matrix Hin, αof OLR-block is sorted as follows: θ ( i ) = arg min j ≠ θ ( k ) k < i h α, j. (17).
Note the change of signs and order of normal vectors.
Explicitly, this implies that each inheritance vector v corresponding to a specific structure A produces the same output (score), i.e. where is the equivalence class including all inheritance vectors corresponding to structure A. Hence considering a pedigree with m corresponding meioses leads to 2 m possible scores, assuming some order of inheritance vectors.
