MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||6|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Feature selected based on PCA and optimized LMC
School of Electronic Information Engineering, Shanghai Dianji University, Shanghai, China
* Corresponding author: firstname.lastname@example.org
In this article, we propose an optimization algorithm for the original LMC  (Large Margin Classifier). We use PCA  (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 . 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.
Key words: PCA / LMC / Optimized LMC / Feature selection
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.