A significant difference between the inner product and correlation coefficient is that correlation coefficient excludes the impact of data mean, so we can consider whether it is feasible if the column vectors of sensing matrix are centered or normalized firstly and still use inner product as screening criterion of matching atoms.
If the column vectors of sensing matrix are centered firstly and still use inner product as screening criterion of matching atoms, the reconstruction probability is silently worse than MMV-ISP algorithm, just as shown in Fig. 10, where MMV-SISP means simplified MMV-ISP.
This matrix is centered by subtracting the column-wise mean.
Prior to principal components analysis, the data matrix is centered such that the column mean with respect to the ancestral reference populations is zero, giving PCA is performed using only individuals from the ancestral populations.
Because we have an intercept parameter in the model, we assume without loss of generality that the design matrix X is centered, (all columns of X have mean 0).
The SKAT test statistic is where y is a vector of phenotypes, is the predicted mean of y under null hypothesis, K = (GW) (GW)' is the SKAT kernel matrix, which depends on the genotype matrix G, assumed to be centered, and a choice of variant weights W. Let T i and T j be two SKAT test statistics.
In matrix algebra, let X0 and Y0 be centered and scaled matrix of predictive and responsive variables; one dummy variable Y indicating clinical phenotype was needed for binary classifications in PLS.
To ensure that no single predictor swamps the effects of others, the matrix of predictors (X) is centered and scaled, and then λ is chosen by cross-validation.
Finally, the LC-MS feature matrix was mean centered and imported into SIMCA-P+ software (Umetrics, Inc., Umeå, Sweeden).
