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Multiple textural features based on histogram and gray level co-occurrence matrix were extracted from the segmented images and ranked them automatically using the sequential forward feature selection method in order to select the best minimal features for the classification purpose.
The principal components of the feature matrix were extracted and the projection of the feature matrix in the first two principal components is shown in Figure 2.
Interaction data used to build the yeast interaction matrix were extracted from [ 64].
For PCR, the principal components associated with the largest eigenvalues of the X' X matrix were extracted and used to predict the y values.
Before each experiment, the rods in the matrix were extracted by a strong permanent magnet, washed by water 2 3 times, and prepared to be mixed with reaction solutions.
Also, the first two principal coordinates of the resulting Jaccard matrix were extracted to display the position of the accessions in a two-dimensional space (as an indication of the diversity of each pair of the accessions).
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Set 2 consisted of post-extraction spiked samples (blank matrix was extracted and then spiked with standards).
Note however that only very few measures on the co-occurrence matrix are extracted.
The eigenvector of the adjacency matrix is extracted from its Laplace matrix and is subsequently used for clustering.
Color histogram and texture features based on a co-occurrence matrix are extracted to form feature vectors.
In FE methods, by the use of principal component analysis (PCA), the information contained in the descriptors data matrix is extracted into new orthogonal variables with lower dimensions.
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