As data sources become ever more numerous with increased feature dimensionality, feature selection for multiview data has become an important technique in machine learning. Semi-supervised multiview feature selection (SMFS) focuses on the problem of how to obtain a discriminative feature subset from heterogeneous feature spaces in the case of abundant unlabeled data with little labeled data. Most existing methods suffer from unreliable similarity graph structure across different views since they separate the graph construction from feature selection and use the fixed graphs that are susceptible to noisy features. Furthermore, they directly concatenate multiple feature projections for feature selection, neglecting the contribution diversity among projections. To alleviate these problems, we present an SMFS to simultaneously select informative features and learn a unified graph through the data fusion from aspects of feature projection and similarity graph. Specifically, SMFS adaptively weights different feature projections and flexibly fuses them to form a joint weighted projection, preserving the complementarity and consensus of the original views. Moreover, an implicit graph fusion is devised to dynamically learn a compatible graph across views according to the similarity structure in the learned projection subspace, where the undesirable effects of noisy features are largely alleviated. A convergent method is derived to iteratively optimize SMFS. Experiments on various datasets validate the effectiveness and superiority of SMFS over state-of-the-art methods.

The use of unmanned aerial vehicles (UAVs) for target searching in complex environments has increased considerably in recent years. Numerous studies on UAV search methods have been reported, but few have been conducted on collaborative human-UAV search which is common in many applications. In this paper, we present a problem of collaborative human-UAV search for escaped criminals, the aim of which is to minimize the expected time of capture rather than detection. We show that our problem is much more complex than the problem of pure UAV search. The difficulty of our problem is further increased by the fact that criminals will attempt to avoid detection and capture. To solve the problem, we propose a hybrid evolutionary algorithm (EA) that uses three evolutionary operators, namely comprehensive learning, variable mutation, and local search, to efficiently explore the solution space. Experimental results demonstrate that the proposed method outperforms some well-known EAs and other popular UAV search methods on test instances. An application of our method to a real-world operation took 311 minutes to capture a criminal who had escaped for over three days, validating its practicability and performance advantage. This study provides a good basis for promoting the application of EAs to a wider class of man-machine collaboration scheduling problems.

This paper studies the set-membership state estimation (SMSE) problem for a class of complex networks under the event-triggered mechanism, where the external unknown-but-bounded (UBB) noises reside within a sequence of zonotopes. For the purpose of saving energy and reducing transmission burden, an event-triggered mechanism is employed for each node of the complex networks to schedule the signal transmissions between the nodes and the remote estimator. The aim of this paper is to design a set-membership estimator such that, in the presence of the UBB noises and the event-triggered scheduling scheme, the resultant estimation error is restrained into a set of zonotopes. According to the properties of zonotopes, a set of zonotopes confining the estimation error is first obtained by using the mathematical induction approach, and then the desired estimator parameters are recursively calculated by minimizing the $F$ -radii of such a zonotopic set. Finally, the effectiveness of the proposed zonotopic SMSE algorithm is demonstrated through an illustrative example.

This article is concerned with the problem of finite-horizon H _∞ state estimation for time-varying coupled stochastic networks through the round-robin scheduling protocol. The inner coupling strengths of the considered coupled networks are governed by a random sequence with known expectations and variances. For the sake of mitigating the occurrence probability of the network-induced phenomena, the communication network is equipped with the round-robin protocol that schedules the signal transmissions of the sensors' measurement outputs. By using some dedicated approximation techniques, an uncertain auxiliary system with stochastic parameters is established where the multiplicative noises enter the coefficient matrix of the augmented disturbances. With the established auxiliary system, the desired finite-horizon H _∞ state estimator is acquired by solving coupled backward Riccati equations, and the corresponding recursive estimator design algorithm is presented that is suitable for online application. The effectiveness of the proposed estimator design method is validated via a numerical example.