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Trusted Multi-view Learning with Noisy Labels: Mitigating the Impact of Inaccurate Guidance


Core Concepts
A reliable multi-view learning model can be developed under the guidance of noisy labels by leveraging multi-view consistent information for detecting and refining noisy labels, and assigning higher decision uncertainty to instances belonging to easily mislabelled classes.
Abstract
The paper introduces the Generalized Trusted Multi-view Learning (GTML) problem, which aims to develop a reliable multi-view learning model under the guidance of noisy labels. To address this problem, the authors propose the Trusted Multi-view Noise Refining (TMNR) method. Key highlights: TMNR first constructs view-specific opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. TMNR then designs view-specific noise correlation matrices to transform the original opinions into noisy opinions aligned with the noisy labels. The diagonal elements of these matrices are inversely proportional to the uncertainty, while the off-diagonal elements incorporate class relations. TMNR aggregates the noisy opinions and employs a generalized maximum likelihood loss on the aggregated opinion for model training, guided by the noisy labels. Experiments on 5 publicly available datasets show that TMNR outperforms state-of-the-art trusted multi-view learning and label noise learning baselines on accuracy, reliability and robustness.
Stats
The higher the uncertainty of the model on the decision, the higher the probability that the sample label is noisy. Samples in the same class can be easily labeled as the same error class, so the transfer probabilities of their non-diagonal elements should be close.
Quotes
"The higher the uncertainty of the model on the decision, the higher the probability that the sample label is noisy." "Samples in the same class can be easily labeled as the same error class, so the transfer probabilities of their non-diagonal elements should be close."

Key Insights Distilled From

by Cai Xu,Yilin... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11944.pdf
Trusted Multi-view Learning with Label Noise

Deeper Inquiries

How can the proposed TMNR method be extended to handle more complex label noise patterns, such as class-conditional noise or structured noise

The TMNR method can be extended to handle more complex label noise patterns by incorporating techniques to address class-conditional noise or structured noise. For class-conditional noise, the method can be adapted to include specific mechanisms that account for the probability of mislabeling based on the class of the instance. This can involve adjusting the noise correlation matrices to consider the likelihood of a sample being mislabeled as a different class based on the true class. By incorporating class-specific noise patterns into the correlation matrices, the model can better handle instances that are prone to class-conditional noise. In the case of structured noise, the TMNR framework can be enhanced by introducing additional constraints or regularization terms that capture the underlying structure of the noise. This could involve analyzing the patterns of noise across different views and designing the correlation matrices to reflect these structured noise patterns. By incorporating structured noise handling mechanisms, the model can effectively adapt to more complex label noise scenarios.

What are the potential limitations of the uncertainty-guided correlation matrix design, and how can it be further improved to handle more diverse real-world scenarios

The uncertainty-guided correlation matrix design in the TMNR framework may have potential limitations in handling diverse real-world scenarios. One limitation could be the assumption that higher uncertainty directly correlates with a higher probability of noisy labels. While this assumption may hold in many cases, there could be instances where the relationship between uncertainty and label noise is more nuanced. To improve the uncertainty-guided correlation matrix design, one approach could be to incorporate adaptive mechanisms that dynamically adjust the correlation matrices based on the specific characteristics of the dataset. This could involve learning the relationship between uncertainty and label noise from the data itself, allowing the model to adapt to varying noise patterns more effectively. Additionally, introducing more sophisticated techniques, such as incorporating meta-learning or reinforcement learning principles, could enhance the flexibility and adaptability of the correlation matrix design. By allowing the model to learn and update the correlation matrices based on feedback from the training data, the framework can better handle diverse and complex real-world scenarios.

How can the TMNR framework be adapted to other multi-view learning tasks, such as clustering or recommendation, to enhance their robustness against noisy data

The TMNR framework can be adapted to other multi-view learning tasks, such as clustering or recommendation, to enhance their robustness against noisy data by incorporating similar principles of uncertainty-guided learning and noise refinement. For clustering tasks, the TMNR framework can be modified to focus on learning reliable cluster assignments under the guidance of noisy labels. By leveraging multi-view consistent information and uncertainty estimates, the model can refine the clustering results to be more robust to label noise. Additionally, incorporating uncertainty-guided correlation matrices can help improve the clustering performance in the presence of noisy data. In the context of recommendation systems, the TMNR framework can be applied to enhance the accuracy and reliability of recommendations by considering the uncertainty in the data and the potential noise in the labels. By refining the noisy labels and incorporating uncertainty estimates into the recommendation process, the model can provide more trustworthy and robust recommendations to users. This can lead to improved user satisfaction and engagement with the recommendation system.
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