The correlation process aims to find the phase vector that most similar with the measured vector in the sample database.
In one algorithm version, this is accomplished by slowing down the rotational velocity while the gravity vector (considered in the sample frame) is pointing downwards.
In this approach, several methods are inspected, which utilize multiple feature vectors of the sample images in a set to build up a more representative model of faces.
Combining with the estimated angle pair, a small sample database with nine sample vectors can be formulated as shown in Figure 5, where the similarities between measured phase difference vector and the vectors in small sample database are denoted as s i, i = 0, 1, …, 8.
Also, the DROUGHT vectors were very close to the CTRL vectors (the two vectors in root/shoot samples pointed in the same direction and were almost parallel to each other).
We have imputed the missing values by FCM-GO-impute method [ 38]; also all the sample vectors in the dataset are normalized in order to have the zero mean and standard deviation of one.
PC1 was dominated by eight loci across the genome (two, four and two from chromosomes X, 2 and 3, respectively; Table 4A), indicating that eight loci are mainly responsible for separating the two major and the two minor vectors in our sample set.
The maternal and paternal vectors in each sample are also compared with each other, which reveals the location of homozygous regions.
For the allocation vector, however, it is not possible to summarize the sampled vectors in the same way as for the edges, because of the so called label switching problem.
The magnitude of differential transcription of a gene between two samples can be measured by the square root of the Jensen Shannon Divergence (JSD*) between the gene's transcript abundance vectors in each sample.
