Generalized Discriminant Analysis (GDA) shows a powerful nonlinear feature extraction technique by kernel tricks. The size of its kernel matrix increases quadratically with the number of training data. For large training data set, it suffers from computational problem of diagonal and occupies large storage space of kernel matrix. Here, a more efficient nonlinear feature extraction method, Greedy G...Show More
A Kernel Spectral Angle Mapper (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of the remote sensing image. The so-called KSAM algorithm is achieved by introducing the kernel method into the standard Spectral Angle Mapper (SAM) algorithm. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorith...Show More
Fuzzy C-Means (FCM) algorithm is an unsupervised fuzzy clustering method. Clustering results accuracy of the algorithm is affected by equal partition trend of the data sets. When amount of each cluster sample are difference greatly, the optimal solution of the algorithm may not be the correct partition of the data sets. Weighted Fuzzy C-Means (WFCM) algorithm is proposed to overcome this disadvant...Show More
