Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies. However, existing methods encounter several fundamental limitations: 1) neglecting class-level semantic alignment and excessively pursuing pair-wise sample alignment; 2) lacking either pseudo-label reliability consideration or geometric guidance for assessing label correctness; 3) directly quantizing original features affected by domain shift, undermining the quality of learned hash codes. In view of these limitations, we propose Prototype-based Semantic Consistency Alignment (PSCA), a two-stage framework for effective domain adaptive retrieval. In the first stage, a set of orthogonal prototypes directly establishes class-level semantic connections, maximizing inter-class separability while gathering intra-class samples. During the prototype learning, geometric proximity provides a reliability indicator for semantic consistency alignment through adaptive weighting of pseudo-label confidences. The resulting membership matrix and prototypes facilitate feature reconstruction, ensuring quantization on reconstructed rather than original features, thereby improving subsequent hash coding quality and seamlessly connecting both stages. In the second stage, domain-specific quantization functions process the reconstructed features under mutual approximation constraints, generating unified binary hash codes across domains. Extensive experiments validate PSCA's superior performance across multiple datasets.

Label prototype learning has emerged as an effective paradigm in Partial Multi-Label Learning (PML), providing a distinctive framework for modeling structured representations of label semantics while naturally filtering noise through prototype-based label confidence estimation. However, existing prototype-based methods face a critical limitation: class prototypes are the biased estimates due to noisy candidate labels, particularly when positive samples are scarce. To this end, we first propose a mutually class prototype alignment strategy bypassing noise interference by introducing two different transformation matrices, which makes the class prototypes learned by the fuzzy clustering and candidate label set mutually alignment for correcting themselves. Such alignment is also passed on to the fuzzy memberships label in turn. In addition, to eliminate noise interference in the candidate label set during the classifier learning, we use the learned permutation matrix to transform the fuzzy memberships label for learning a label reliability indicator matrix accompanied by the candidate label set. This makes the label reliability indicator matrix absolutely prevent the occurrence of numerical values located in non-label and simultaneously eliminate the introduction of incorrect label as much as possible. The resulting indicator matrix guides a robust multi-label classifier training process, jointly optimizing label confidence and classifier parameters. Extensive experiments demonstrate that our proposed model exhibits significant performance advantages over state-of-the-art PML approaches.

This work proposes a fully distributed moving horizon estimation method with an event-triggered communication strategy over wireless sensor networks. The proposed method calculates a local state estimation of each sensor by minimizing a quadratic objective function, which involves a fused arrival cost that is computed in a distributed manner. This approach adjusts data transmission rate by selectively transmitting local information and allows the incorporation of constraints on the noise and state variables. In addition, the estimation error is proved to be uniformly bounded in the mean square sense under the condition that the network topology is strongly connected and the system is collectively observable. Finally, a target tracking example is presented to demonstrate the validity of the proposed approach.

Partial multi-label learning (PML) aims to accurately predict multi-labels for unknown instances with noise labels in training set. Accurate identification of ground-truth labels is essential to optimize performance. The accuracy of ground-truth labels affects label correlation capture, which guides classifier learning. However, current methods often rely on approximation of ground-truth labels through intermediate variables, rather than restoring binary ground-truth labels directly. To solve this problem, we propose a partial multi-label learning algorithm by exploring binary ground-truth labels (PML-EBGL). First, the candidate label matrix is decomposed into a ground-truth label matrix and a noise matrix. Then, the noise matrix is constrained using the l1norm for sparsity, and the binary ground-truth label matrix is used to explore label correlation, which is transferred to classifier by using the Laplacian term. Finally, the rotation matrix is added after the classifier, and the instance is projected onto the binary label matrix. Extensive experiments show that PML-EBGL outperforms state-of-the-art methods.