When measuring the importance of attributes in Tensor-based Multiple Clustering algorithm （TMC）， the relevance of attribute combinations within object tensors are ignored， and the selected and unselected feature space are incompletely separated because of the fixed weight strategy under different feature space selection. For above problems， a Multiple Clustering algorithm based on Dynamic Weighted Tensor Distance （DWTD-MC） was proposed. Firstly， a self-association tensor model was constructed to improve the accuracy of attribute importance measurement of each feature space. Then， a multi-view weight tensor model was built to meet the task requirements of multiple clustering analysis by dynamic weighting strategy under different feature space selection. Finally， the dynamic weighted tensor distance was used to measure the similarity of data points， generating multiple clustering results. Simulation results on real datasets show that DWTD-MC outperforms comparative algorithms such as TMC in terms of Jaccard Index （JI）， Dunn Index （DI）， Davies-Bouldin index （DB） and Silhouette Coefficient （SC）. It can obtain high quality clustering results while maintaining low redundancy among clustering results， as well as meeting the task requirements of multiple clustering analysis.

Because multiple kernel learning can avoid selection of kernel functions and parameters effectively， and graph clustering can fully mine complex structural information between samples， Multiple Kernel Graph Clustering （MKGC） has received widespread attention in recent years. However， the existing MKGC methods suffer from the following problems： graph learning technique complicates the model， the high rank of graph Laplacian matrix cannot ensure the learned affinity graph to contain accurate c connected components （block diagonal property）， and most of the methods ignore the high-order structural information among the candidate affinity graphs， making it difficult to fully utilize the multiple kernel information. To tackle these problems， a novel MKGC method was proposed. First， a new graph learning method based on capped simplex projection was proposed to directly project the kernel matrices onto graph simplex， which reduced the computational complexity. Meanwhile， a new block diagonal constraint was introduced to keep the accurate block diagonal property of the learned affinity graphs. Moreover， the low-rank tensor learning was introduced in capped simplex projection space to fully mine the high-order structural information of multiple candidate affinity graphs. Compared with the existing MKGC methods on multiple datasets， the proposed method has less computational cost and high stability， and has great advantages in Accuracy （ACC） and Normalized Mutual Information （NMI）.

For the problem that the existing multi-source adaptation learning schemes cannot effectively distinguish the useful information in multi-source domains and transfer the information to the target domain, a Multi-source Adaptation Classification Framework with Feature Selection (MACFFS) was proposed. Feature selection and shared feature subspace learning were integrated into a unified framework by MACFFS for joint feature learning. Specifically, multiple source domain classification models were learned and obtained by MACFFS through mapping feature data from multiple source domains into different latent spaces, so as to realize the classification of target domains. Then, the obtained multiple classification results were integrated for the learning of the target domain classification model. In addition, L _2,1 norm sparse regression was used to replace the traditional least squares regression based on L ₂ norm by the framework to improve the robustness. Finally, a variety of existing methods were used to perform experimental comparison and analysis with MACFFS in two tasks. Experimental results show that, compared with the best performing Domain Selection Machine (DSM) in the existing methods, MACFFS has nearly 1/4 of the calculation time saved, and the recognition rate of about 2% improved. In general, with machine learning, statistical learning and other related knowledge combined, MACFFS provides a new idea for the multi-source adaptation method. Furthermore, this method has better performance than the existing methods in recognition applications in real scenes, which had been experimentally proven.

Concerning the problems of large node memory occupation, complex calculation, low information safety degree, in the legal identity authentication when new node joins in sensor networks, a node authentication mechanism of highly safety degree applicable to the limited memory network was proposed. The mechanism used the password to add the node itself, and one-way Hash function was applied to the password and IDentity (ID) for hashing. Password was involved in the generation of the elliptic curve signature algorithm and authentication scheme of congruence equation was adopted between credible nodes. Each certification stage used mutual authentication mode. The proposed algorithm not only can prevent eavesdropping, replay, injection and so on, but also is able to resist guessing attack, mediation attack, anonymous attack and denial of service attack. The comparison with the existing algorithms show that the proposed scheme can reduce the node original memory occupation of three unit level and can reduce key detection rate.