Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

Algorithm 1

Supervised locally linear embedding method description.

Input:

Output: Reduction set

Step  1. For each data in high-dimensional space, find the nearest points in terms of the Euclidean distance;

Step  2. Calculate the local reconstructed weight matrix for each sample point. The current sample point is expressed by the nearest

neighboring points and gets the weight matrix, the error function is defined as: ;

Step  3. According to the weight for the sample point and neighboring point in the high-dimensional space. Then the

embedding space in low-dimension is calculated. The weight is fixed to a constrained optimization problem;

Step  4. By minimizing the loss functions to get the corresponding weight matrix and reconstructed coordinates. The retained

eigenvectors are formed the output of LLE algorithm;

Step  5. Return reduction set.