Drone Classification Based on Fuzzy Locality Preserving Projection

Locality Preserving Projection (LPP) is one of the most promising manifold learning methods, which is commonly used for feature extraction. But its purpose is only to preserve local distance information between samples, and does not consider the class information of samples that plays a crucial role in classification tasks, which leads to low recognition performance of LPP. In view of this, a new feature extraction method called Fuzzy Locality Preserving Projection (FLPP) is proposed for the classification of drone in this paper. The advantage of FLPP is that it can fully utilize the distribution information and the class information of the data samples by incorporating a fuzzy membership matrix into the neighborhood graph of LPP, and thus can improve the recognition performance. Experimental results show that the average recognition rate of the proposed FLPP is improved by 15.93%, 4.32%, and 3.25% compared with the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and LPP methods, respectively.