Research on New Methods of Linear Discriminant Analysis and Their Applications
|School||Nanjing University of Aeronautics and Astronautics|
|Course||Applied Computer Technology|
|Keywords||Pattern Recognition Linear discriminant analysis Rank restrictions Matrix singularity Threshold selection Binarization Image processing|
Fisher linear discriminant analysis (FLDA, or LDA) is one of the very effective feature extraction method has been widely and successfully applied in the field of dimensionality reduction , data analysis and pattern classification . Conducted a series of studies on linear discriminant analysis and its application . First of all , on the basis of the multi-feature linear discriminant analysis (Multi-feature LDA, MFLDA) , nuclear - based multi-feature linear discriminant analysis ( kernel MFLDA kMFLDA ) to overcome the multi-feature linear discriminant analysis can not be effectively solve nonlinear separable problem is inadequate; Secondly, the rank restrictions exist in the traditional linear discriminant analysis and singularity problem by amending the original LDA criteria , proposed a modified linear discriminant analysis criteria (Modified LDA , ModLDA), in order to overcome the the rank limitations and relaxation of the restrictions of the singularity problem . The experimental results show that this paper, two linear discriminant analysis method is superior to the traditional LDA or equivalent effect in a number of international standard data sets and artificial datasets . Finally, linear discriminant analysis conducted applied research in the gray image binarization problem . Grayscale image objects usually have a uniform grayscale value , while the background generally has a non-uniform gray value features , from human intuition of the observed image , the design of a new two types of criterion , by optimizing the the criteria to obtain the optimal threshold value , and then use this threshold value to achieve a gray image binarization . The comparison experiments show that with the other methods : image segmentation obtained by this method has a better visual effect .