As a basic machine learning task, Multi-View Classification (MVC) has garnered considerable attention and achieved great success. However, the existing MVC methods, especially late fusion style ones still suffer from some problems: 1) hidden valuable information is not well exploited; 2) a lack of interaction before decision making. To address these problems, we propose a novel framework named ”TrashtoTreasure” that leverages mutual information to effectively exploit hidden valuable information. Specifically, the framework explicitly disentangles multi-view information into ”useful” components and ”trash” (noisy) components, and further extracts potentially valuable ”treasure” information from the ”trash”components of all views. Additionally, we design a tailored objective function that facilitates the effective separation of ”useful” and ”trash” components, as well as the synergistic extraction of ”treasure” information. This function guides model optimization through triple mutual information constraints. Experimental results on synthetic data and several real-world data sets verified the effectiveness and superiority of the proposed method. The fresh perspective offered by this article may inspire more interesting exploration in this direction. The codes are available athttps://github.com/jiezhang054/TrashToTreasure.

Spatial Transcriptomics offers unprecedented opportunities to explore tissue architecture by capturing gene expression with spatial context. However, effectively learning discriminative and spatially smooth representations for accurate spatial domain identification remains a significant challenge. To address this, we propose CSMVL, a multi-view representation learning framework to learn high-quality spot representations by synergistically enhancing both discriminability and spatial continuity. CSMVL introduces a cluster structure learning strategy that guides cell representations within the same domain toward their cluster center while simultaneously separating distinct cluster centers, thereby improving intra-domain compactness and inter-domain separability. Furthermore, graph smoothness regularization is introduced to ensure that representations of spatially adjacent cells within the same domain transition smoothly, reflecting the inherent spatial continuity of biological tissues. Extensive experiments on public ST datasets demonstrate CSMVL's superiority, achieving an average ARI of 71.64% and NMI of 73.43%, outperforming existing state-of-the-art methods

Cross-modal retrieval is a promising technique nowadays to find semantically similar instances in other modalities while a query instance is given from one modality. However, there still exists many challenges for reducing heterogeneous modality gap by embedding label information to discrete hash codes effectively, solving the binary optimization when generating unified hash codes and reducing the discrepancy of data distribution efficiently during common space learning. In order to overcome the above-mentioned challenges, we propose a Collaboratively Semantic alignment and Metric learning for cross-modal Hashing (CSMH) in this paper. Specifically, by a kernelization operation, CSMH first extracts the non-linear data features for each modality, which are projected into a latent subspace to align both marginal and conditional distributions simultaneously. Then, a maximum mean discrepancy-based metric strategy is customized to mitigate the distribution discrepancies among features from different modalities. Finally, semantic information obtained from the label similarity matrix, is further incorporated to embed the latent semantic structure into the discriminant subspace. Experimental results of CSMH and baseline methods on four widely-used datasets show that CSMH outperforms some state-of-the-art hashing baseline methods for cross-modal retrieval on efficiency and precision.

In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.