Algorithm 2 ANOVA F-spectral Embedding

Input: $X_I$.

Output: $X_o$

1   $X_I$ is the sample image feature matrix, $X_o$ is the selected and transformed sample image feature matrix.

2   FOR i = 1: n //n is the dimension of a feature in the feature matrix of the image sample.

3   Calculate the $f$-value of each feature according to Formula (18).

4   $f_{vector}=f_{vector} U f$

5   $su{m}_f+=f_i$//$su{m}_f$ is the sum of the f-values of the sample features, $f_i$ is the f-value of the i-th feature.

6  END

7  Calculate the ${\overline{f}}_{vector}$ value of each feature according to Formula (18), sort ${\overline{f}}_{vector}$ in descending order

8 FOR i = 1: n

9  IF (sum + = ${\overline{f}}_{vector}\left(i\right)$) < 99.9%

10   $X$ = $X_I$($i$)

11   END

12 END

13  Transform $X$ into a graph representation using the affinity (adjacency) matrix representation.

14  Construct an unnormalized Laplacian graph as $L=D-A$ and a normalized graph as $L=D^{-1/2}\left(D-A\right)D^{-1/2}$.

15  Perform eigenvalue decomposition on the Laplacian graph after performing the above treatment on $X_o$.

16 Return $X_o$