Secondly, we add a weight factor β before the taking-back-noise term of the updating iteration step, i.e. b_{k + 1} = b_{k} + betapi_{lambda K}(g + b_{k}).
Through the updating iterations, the algorithm searches in the space of all possible mapping configurations where each mapping configuration can be considered as a bipartite graph with edges connecting tags and sites (Fig. 1).
On its face it's a scaled-back, updated iteration of the United Nation Orchestra, featuring the trumpeter Amir ElSaffar, the saxophonists David S?hez and Rudresh Mahanthappa, the bassist Ben Street, the drummer Adam Cruz and the percussionist Jamey Haddad.
Because of the (relaxed) cyclic iteration method, an updated iteration number after all constrained sets is calculated.
As a final step, the R matrix is kept fixed, and the A matrix is iteratively updated for I 3 rounds based on Y≈R S A. To demonstrate the behavior of the algorithm described above, Fig. 3 illustrates the cost function of Eq. (10) as a function of the update iterations.
In each global Newton iteration, the updated global variables P, X, and T from the previous iteration were passed to the EOS system, and acted as constraints to solve for secondary variables.
Due to the random initialization of clusters, we use a loose p value cutoff at the beginning and decrease it iteration by iteration as the updated cluster profiles become more stable and reflect the authentic clusters more reliably as the clustering process progresses.
The details of the AO algorithm is shown in Algorithm 1, where WSR[n] is the WSR calculated based on the updated (x,P) in nth iteration.
In the next iteration, the updated local information becomes part of the global information and another local update is executed on a different model parameter.
If the standard deviation of a cluster is σ(t ) and in next iteration, the updated value σ(t +1) satisfies 0.9σ(t ) ≤ σ(t +1) ≤ 1.1σ(t ), then for this cluster, we consider where μ is the center of this cluster and x is a sequence in this cluster.
The updated prediction at each iteration m may be expressed as F m(x i) = F m-1(x i)+ v· h y i; x i, j m) with v being some shrinkage factor that, without loss of generality, can be assumed constant and small (0< v <1), but it may be optimized to balance predictive ability and computation time.
