Many network structure information-based link prediction algorithms estimate the similarity between nodes and perform link prediction by using the clustering degree of nodes. However, these algorithms only focus on the clustering coefficient of nodes in network, and do not consider the influence of link clustering coefficient between the predicted nodes and their common neighbor nodes on the similarity between nodes. Aiming at the problem, a link prediction algorithm combining node clustering coefficient and asymmetric link clustering coefficient was proposed. Firstly, the clustering coefficient of common neighbor node was calculated, and the average link clustering coefficient of the predicted nodes was obtained by using two asymmetric link clustering coefficients of common neighbor node. Then, a comprehensive measurement index was obtained by fusing these two clustering coefficients based on Dempster-Shafer(DS) theory, and by applying the index to Intermediate Probability Model (IMP), a new node similarity index, named IMP_DS, was designed. The experimental results on the data of nine networks show that the proposed algorithm achieves performance in terms of Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) and Precision in comparison with Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA) indexes and InterMediate Probability model based on Common Neighbor (IMP_CN).

To solve the time-consuming and error-prone problem in the diagnosis of fundus images by the ophthalmologists, an unsupervised automatic detection method for hard exudates in fundus images was proposed. Firstly, the blood vessels, dark lesion regions and optic disc were removed by using morphological background estimation in preprocessing phase. Then, with the image luminosity channel taken as the initial image, the low rank matrix and sparse matrix were obtained by combining local entropy and Robust Principal Components Analysis (RPCA) based on the locality and sparsity of hard exudates in fundus images. Finally, the hard exudates regions were obtained by the normalized sparse matrix. The performance of the proposed method was tested on the fundus images databases e-ophtha EX and DIARETDB1. The experimental results show that the proposed method can achieve 91.13% of sensitivity and 90% of specificity in the lesional level and 99.03% of accuracy in the image level and 0.5 s of average running time. It can be seen that the proposed method has higher sensitivity and shorter running time compared with Support Vector Machine (SVM) method and K-means method.

The existing multivariate time series classification algorithms require sequences of equal length and neglect categories information. In order to solve these defects, a multivariate time series classification algorithm was proposed based on Singular Value Decomposition (SVD) and discriminant locality preserving projection. Based on the idea of dimension reduction, the first right singular vector of samples by SVD was used as feature vector to transform unequal length sequence into a sequence of identical size. Then the feature vector was projected by utilizing discriminant locality preserving projection based on maximum margin criterion, which made full use of categories information to ensure samples of the same class as close as possible and heterogeneous samples as dispersed as possible. Finally, it achieved the classification in a low dimension subspace by using 1 Nearest Neighbor (1NN), Parzen windows, Support Vector Machine (SVM) and Naive Bayes classifier. Experiments were carried out on Australian Sign Language (ASL), Japanese Vowels (JV) and Wafer, the three public multivariate time series datasets. The results show that the proposed algorithm achieves lower classification error rate under the condition of the same time complexity basically.