Research Article
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. |
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