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Table 1 Arrangement of features into m × m matrix

From: 2D–EM clustering approach for high-dimensional data through folding feature vectors

Feature Selection

 1. Given x χ in a d-dimensional space.

 2. Perform hierarchical clustering on all samples x to find temporary class labels.

 3. Using these class labels find p-values for all the d features.

 4. Find m by placing a threshold or cut-off on p-values (e.g. cut-off for p-values could be 0.01).

 5. Retaining the top m 2 features will give us a sample \( y\in {\mathrm{\mathbb{R}}}^{m^2} \), where all y samples form a sample set \( Y\in {\mathrm{\mathbb{R}}}^{m^2\times n} \).

Matrix arrangement

 6. Compute mean \( {\mu}_y=\frac{1}{n}\sum \limits_{y\in Y}y \).

 7. Arrange features of μ y in ascending order and note the indices.

 8. Arrange features of y by following the indices from step 7.

 9. Reshape a sample y to a matrix Xm × m.