A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and -Nearest Neighbor Graph

Algorithm 1

Procedure of SDA using combined low-rank and -nearest neighbor graph.

Input: The whole dataset , where samples are labeled

and are unlabeled ones.

Output: The classification results.

Step 1. Map the labeled and unlabeled data to feature space by the LRR algorithm.

Step 2. Obtain the symmetric graph by -nearest neighbor algorithm.

Step 3. Implement the SDA algorithm for dimensionality reduction.

Step 4. Execute the nearest neighbor approach for the final classification.