Feature selected based on PCA and optimized LMC

Abstract

In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into the optimized LMC for the feature selection [3]. We will get several features with the greatest distinction. We use these features to classify images. Finally, the experiment shows that the accuracy of the optimized LMC under the same dimensions is higher than that of the original LMC, and in many cases, the accuracy of the optimized LMC after taking 6 feature vectors has exceeded the highest accuracy of the original LMC.

Keywords

• pca
• lmc
• optimized lmc
• feature selection