Deep Partial Multi-View Learning (2011.06170v1)

Abstract: Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and flflexibly take advantage of multiple partial views. We fifirst provide a formal defifinition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the learned latent representations. For completeness, the task of learning latent multi-view representation is specififically translated to a degradation process by mimicking data transmission, such that the optimal tradeoff between consistency and complementarity across different views can be implicitly achieved. Equipped with adversarial strategy, our model stably imputes missing views, encoding information from all views for each sample to be encoded into latent representation to further enhance the completeness. Furthermore, a nonparametric classifification loss is introduced to produce structured representations and prevent overfifitting, which endows the algorithm with promising generalization under view-missing cases. Extensive experimental results validate the effectiveness of our algorithm over existing state of the arts for classifification, representation learning and data imputation.

• The paper presents CPM-Nets, a novel framework that reconstructs shared latent representations from incomplete views.
• The paper employs a structured classification loss and adversarial imputation strategy to enhance data representation robustness.
• The paper demonstrates superior performance across diverse datasets, notably excelling even at a 50% view missing rate.

Deep Partial Multi-View Learning: An Expert Summary

The paper "Deep Partial Multi-View Learning" introduces a novel framework, Cross Partial Multi-View Networks (CPM-Nets), designed to address challenges inherent in multi-view learning where views may be incomplete or missing. Multi-view data is common in scientific and practical domains, including contexts like medical imaging or web analysis, where different subjects may have different modalities available. Conventional multi-view learning methods assume that all views are available for each sample, which is often not the case in real-world applications. CPM-Nets offer a robust solution by leveraging incomplete views and jointly optimizing data representation for subsequent learning tasks.

Framework Design and Contributions

CPM-Nets are structured to enhance both the completeness and versatility of latent representations in multi-view data. The distinct components of this model include:

1. Completeness in Representation: CPM-Nets achieve completeness by ensuring that each observation from any view can be reconstructed from a shared latent representation. This robustness is critical in environments with extensive data missingness and complex inter-view correlations.
2. Structured Classification Loss: By incorporating a nonparametric clustering-like classification loss, CPM-Nets ensure that latent representations are well-structured, making them adequately separated and compact according to class distributions.
3. Adversarial Strategy for Imputation: The model enhances robustness against view missingness with an adversarial strategy that stabilizes imputation through learned distribution alignment, thus improving latent representation simultaneously with view completions.

Numerical Results and Implications

The numerical results are indicated through extensive experimental evaluations across multiple datasets with different modalities, like Animal, Handwritten, CUB, 3Sources, and real-world missing data like ADNI. These results highlight CPM-Nets' superior performance, maintaining higher accuracy and robust imputation across varied missing rates as compared to contemporary methods. For example, under a 50% view missing rate, CPM-Nets outperform other methods significantly, indicating robust imputation capability and competitiveness of the framework for clustering and classification tasks across missing modality scenarios.

Speculations on Future Directions

The results and methodologies presented pave the way for further research in the field of multi-view learning by establishing a framework that flexibly handles arbitrary view missingness patterns. The advances implied here may extend to enhancing predictive analytics across fields like healthcare, finance, and multimedia, where data is often incomplete or collected sporadically. Furthermore, future theoretical analyses could solidify CPM-Nets' robustness in various complex real-world applications.

In conclusion, the paper achieves a critical advancement in multi-view learning methodologies, providing an adaptable framework for comprehensive representation and more structured analysis in incomplete data domains. This strongly indicates potential for further paper on leveraging latent representations in broader AI applications, optimizing the trade-off between data consistency and learning accuracy.