Sentence examples for sparse input from inspiring English sources

Finally, we extended a well-known statistical test for cyclostationarity to accommodate sparse input.

Finally, a dynamic Kriging-based optimization approach constructs a multidimensional response surface using sparse input datasets; the response surface is then used to identify an optimal design.

Note that, seen as a generative model, Equation 1 is fairly simple: a highly sparse input distorted by an SV blur, even linear for a given pattern field.

Though the dataset sparsity is not affected by clustering, one can assume that the generated sub-spaces are simpler to classify than the full sparse input space.

In this paper, we present our research in this direction, extending the one-dimensional method for cache-oblivious SpMV multiplication to two dimensions, while still allowing only row and column permutations on the sparse input matrix.

In the previous section, it was initially assumed that X c =X k =X k+N/2 for odd k positions, since they were derived from sparse input data.

We look for Φ matrices with as few rows as possible which can guarantee the invertibility, stability, and robustness of the sampling process for the class of sparse inputs.

In this section, we formulate UPGMA for sparse inputs, and give a suitable algorithm— Sparse-UPGMA (Fig. 2).

(1) Equation (1) (or any other average-linkage formulation) is not well defined for sparse inputs (i.e. when e pq ∉𝔼).

We define a (UPGMA) clustering solution as exact (or correct), if the order of merges is correct (up to equidistant merges), i.e. it always yields the same solution as UPGMA (or Sparse-UPGMA for sparse inputs), regardless of computational limitations such as memory requirements.

Figure 5 Reconstruction SNR versus input SNR in noisy compressed sensing for 70-sparse input signals.