Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Algorithm 3

LDA steps.

1. Computing the within-class and between-class scatter matrices.

2. Computing the eigenvectors and their corresponding eigenvalues for the scatter matrices.

3. Sorting the eigenvalues and selecting the top k.

4. Creating a new matrix that will contain the eigenvectors mapped to the k eigenvalues.

5. Obtaining new features by taking the dot product of the data and the matrix from step.