Multi-view Hashing Classification

Multi-view classification aims to leverage information from multiple views of data to improve prediction performance by learning complementary and consistent representations. Therefore, in recent years, multi-view learning has attracted widespread attention in the community. Despite the success of existing multi-view learning methods, there are still some challenges when dealing with large-scale multi-view data. To address this issue, we propose a novel Multi-view Hashing Classification (MHC) framework to encode large-scale multi-view data as binary codes, thereby enhancing the semantic discrimination. Specifically, we leverage class prompts to generate corresponding textual descriptions for each instance and learn the corresponding anchor hash codes. To achieve intra-class compactness and inter-class separability, we propose Class-prompt Contrastive Learning (CCL) to enforce class-wise aggregation and separation in the Hamming space. To mitigate the cross-view heterogeneity gap, we propose a Supervised Cross-view Contrastive (SCC) module to align view-specific hash codes under label supervision. Finally, we present Boundary-aware Independent Hashing (BIH) that introduces boundary-aware constraints to reduce class boundary ambiguity, thereby improving the discrimination of fusion hash codes. Nevertheless, we observe that anchor hash codes could violate the bit independence assumption, which potentially hinders the optimization direction. To this end, we adopt a Bit-level Calibration Mechanism (BCM) to filter out redundant bits, thereby restoring bit independence. Extensive experiments conducted on ten benchmark datasets demonstrate the superiority of the proposed MHC in terms of both classification accuracy and inference efficiency. The code is released at https://github.com/Yuhang-lan04/MHC.