Feature extraction can overcome the Hughes phenomenon for hyperspectral image classification. Linear discriminant analysis (LDA) is a basic supervised feature extraction method. However, LDA only cannot extract features more than number of classes. The semi-supervised local discriminant analysis (SELD) was proposed to solve the above problem by combing the scatter matrices of LDA and the neighborhood preserving embedding (NPE). Some unlabeled samples were used to form the scatter matrices of NPE. It can preserve the local geometric property according to the used unlabeled samples. Moreover, the between-class scatter matrix of SELD is nonsingular, and more features can be extracted by applying SELD. However, in SELD, the unlabeled sample were randomly selected. The local geometric property around the training samples cannot be preserved due to the randomly selection. In this study, the concept of the Voronoi diagram is used to determine the regions according to the training samples, and the unlabeled samples are chosen in the regions based on the nearest neighbors. Experimental results on the Indian Pine Site dataset show that the proposed method outperforms SELD with less number of unlabeled samples on the small sample size problem.

In this paper, we propose two intelligent leaky bucket algorithms for sustainable-cell-rate usage parameter control of multimedia transmission in asynchronous transfer mode networks. One is the fuzzy leaky bucket algorithm, in which a fuzzy increment controller (FIC) is incorporated with the conventional leaky bucket algorithm; the other is the neural fuzzy leaky bucket algorithm, where a neural fuzzy increment controller (NFIC) is added with the conventional leaky bucket algorithm. Both the FIC and the NFIC properly choose the long-term mean cell rate and the short-term mean cell rate as input variables to intelligently determine the increment value. Simulation results show that both intelligent leaky bucket algorithms have significantly outperformed the conventional leaky bucket algorithm, by responding about 160% faster when taking control actions against a nonconforming connection while reducing as much as 50% of the queueing delay experienced by a conforming connection. In addition, the neural fuzzy leaky bucket algorithm outperforms the fuzzy leaky bucket algorithm, in aspects of three performance measures such as selectivity, responsiveness, and queueing delay, especially when the traffic flow is bursty, dynamic, and nonstationary.