Effective multi-view subspace learning is crucial for enhancing classification performance on multi-view data. In this paper, we propose CMvLSCN, a novel end-to-end framework addressing multi-view classification at view, sample, and subspace levels. The key innovations are: Strengthening inter-view consistency within categories while weakening inter-view similarity across categories; Learning a unified latent subspace representation through the view fusion; and Imposing low-rank latent self-representation and hierarchical contrastive constraints to better classify multi-view data. CMvLSCN employs contrastive learning to optimize Kullback-Leibler divergence among views, imposes low-rank structure on the latent subspace, and introduces sample-level contrastive constraints. This approach captures underlying data relationships and enhances subspace representation discriminability. Experiments demonstrate superior performance, especially with limited training data. Code and datasets are available on GitHub.

Anomaly detection plays a pivotal role in industrial quality assurance processes, with cross-domain problems, exemplified by the model upgrade from RGB to 3D, being prevalent in real-world scenarios yet remaining systematically underexplored. To address the severe challenges posed by the extreme lack of datasets in target domain, we retain the knowledge from source models and explore a novel solution for anomaly detection through cross-domain learning, introducing HyperTrans. Targeting few-shot scenarios, HyperTrans centers around hypergraphs to model the relationship of the limited patch features and employs a perturbation-rectification-scoring architecture. The domain perturbation module injects and adapts channel-level statistical perturbations, mitigating style shifts during domain transfer. Subsequently, a residual hypergraph restoration module utilizes a cross-domain hypergraph to capture higher-order correlations in patches and align them across domains. Ultimately, with feature patterns exhibiting reduced domain shifts, an inter-domain scoring module aggregates similarity information between patches and normal patterns within the multi-domain subhypergraphs to make an integrated decision, generating multi-level anomaly predictions. Extensive experiments demonstrate that HyperTrans offers significant advantages in anomaly classification and anomaly segmentation tasks, outperforming state-of-the-art non-cross-domain methods in image-wise ROCAUC by 13%, 12%, and 15% in 1-shot, 2-shot, and 5-shot settings on MVTec3D AD.

Multiview data classification remains a challenging problem in machine learning, particularly in effectively integrating and representing data from different views. This article introduces contrastive multiview low-rank latent subspace self-representation and classification network (CMvLSCN), a novel end-to-end multiview discriminant learning framework that addresses classification from view, sample, and subspace levels. CMvLSCN employs contrastive learning to enhance interview consistency within categories while differentiating between categories. It imposes a low-rank latent self-representation structure on the unified subspace, capturing intrinsic data relationships. Additionally, sample-level contrastive constraints in the latent space further boost the representation’s discriminative power. Extensive experiments demonstrate CMvLSCN’s superior performance across various multiview classification tasks, notably maintaining robustness even with limited training data. Our code and datasets are publicly available on https://github.com/DeyuTsang/CMvLSCN

BACKGROUND: As an irreversible post-translational modification, protein carbonylation is closely related to many diseases and aging. Protein carbonylation prediction for related patients is significant, which can help clinicians make appropriate therapeutic schemes. Because carbonylation sites can be used to indicate change or loss of protein function, integrating these protein carbonylation site data has been a promising method in prediction. Based on these protein carbonylation site data, some protein carbonylation prediction methods have been proposed. However, most data is highly class imbalanced, and the number of un-carbonylation sites greatly exceeds that of carbonylation sites. Unfortunately, existing methods have not addressed this issue adequately. RESULTS: In this work, we propose a novel two-way rebalancing strategy based on the attention technique and generative adversarial network (Carsite_AGan) for identifying protein carbonylation sites. Specifically, Carsite_AGan proposes a novel undersampling method based on attention technology that allows sites with high importance value to be selected from un-carbonylation sites. The attention technique can obtain the value of each sample's importance. In the meanwhile, Carsite_AGan designs a generative adversarial network-based oversampling method to generate high-feasibility carbonylation sites. The generative adversarial network can generate high-feasibility samples through its generator and discriminator. Finally, we use a classifier like a nonlinear support vector machine to identify protein carbonylation sites. CONCLUSIONS: Experimental results demonstrate that our approach significantly outperforms other resampling methods. Using our approach to resampling carbonylation data can significantly improve the effect of identifying protein carbonylation sites.

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.