Local feature-based sparse LDA feature selection algorithm for image data
Yu Meng, Xiaobin Zhi, Chuanlin Niu, Jiulun Fan · 2023
Image is an important class of matrix structured data. In practical application scenarios, local features of data often contain more discriminative information. Considering that the existing sparse feature selection algorithms rarely consider the local structure information contained in image data, this paper proposes a sparse feature selection algorithm for image data based on local feature based sparse linear discriminant analysis (LFSLDA). Firstly, the scale-invariant feature transform (SIFT) local feature descriptor matrix is used to represent the original image matrix data, and then row and column transformation are carried out respectively. In order to better select the features with more discriminant information and no correlation between each other, the algorithm introduces the generalized uncorrelation constraint and applies it to the total divergence matrix, which not only avoids the singularity problem, but also preserves the structure of the data. Experimental results show that this algorithm can make better use of the local feature information of matrix data and have better performance than other related algorithms on six image data sets.