Progressive Identification of True Labels for Partial-Label Learning (2002.08053v3)

Abstract: Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

• The paper introduces a novel, model-agnostic framework for Partial-Label Learning featuring a progressive identification algorithm that improves scalability.
• The framework includes a classifier-consistent risk estimator and a progressive algorithm that empirically outperforms state-of-the-art methods on benchmarks.
• The model-agnostic design enhances flexibility and scalability, crucial for applying Partial-Label Learning efficiently in real-world tasks such as image annotation.

Overview of "Progressive Identification of True Labels for Partial-Label Learning"

The paper "Progressive Identification of True Labels for Partial-Label Learning" introduces an advanced framework for addressing Partial-Label Learning (PLL), a subset of weakly supervised learning where each training instance is provided with a set of candidate labels containing a single true label. Traditional approaches to PLL have typically relied on constrained optimization techniques tailored to specific algorithms, which often encumber computational scalability. This paper presents an innovative methodology designed to be both model and loss function agnostic, thereby enhancing flexibility and scalability with respect to big data contexts.

Key Contributions

1. Classifier-Consistent Risk Estimator: The paper proposes a novel estimator for classification risk that guarantees classifier-consistency. It ensures that the classifier inferred from partial-label data converges to the one learned from fully labeled data under certain mild conditions. This is significant as it facilitates the application of well-established classification principles in an underexplored setting like PLL.
2. Progressive Identification Algorithm: A prominent contribution is a progressive identification algorithm developed to approximately minimize the proposed risk estimator. This algorithm seamlessly integrates model updates with the identification of true labels, proceduralizing them in tandem, which is advantageous over existing EM-based approaches that might suffer from overfitting due to strict separation between EM optimization steps.
3. Theoretical Assurance with Estimation Error Bound: The authors establish an estimation error bound for the proposed approach, providing theoretical validation for its effectiveness. This underlines the method's robustness and reliability as it theoretically converges to the optimal classifier with an increase in sample size.
4. Model and Loss Independence: The proposed solution exhibits independence from specific models and loss functions, accommodating a broad spectrum of classifiers from linear models to deep network architectures. This flexibility addresses the adaptability shortfall observed in some contemporary PLL schemes.

Experimental Validation

Empirically, the paper demonstrates that the proposed methodology outperforms several state-of-the-art PLL approaches. In particular, experiments conducted on benchmark datasets such as MNIST, Fashion-MNIST, Kuzushiji-MNIST, and CIFAR-10 underpin the efficacy of the proposed method. The adaptability of the framework to different model architectures, including linear models, multilayer perceptrons, and advanced convolutional networks, is meticulously validated. Moreover, thorough experiments on both synthetic and real-world partial-label datasets corroborate its superiority in maximizing test accuracy across various noise conditions and configurations.

Implications and Future Directions

The contributions of this research are crucial for expanding the scalability and efficiency of weakly supervised learning paradigms in broader applications like automatic image annotation, web mining, and beyond where label sparsity is a norm. The advancement towards methods that are not tightly coupled with specific models further empowers the deployment of PLL in diverse application scenarios across industries.

For future exploration in AI, the integration of the proposed PLL approach with other machine learning techniques, such as transfer learning and active learning, could be an interesting trajectory. Additionally, extending the framework to multi-label or hierarchical label ecosystems, or enhancing it with adaptability to rapidly changing data distributions, could offer substantial benefits.

In conclusion, this paper presents a strategic step forward in PLL, offering a framework that aligns with the demands for flexibility and scalability in contemporary AI systems. Through classifier-consistency, error bounds, and empirical success, the proposed method positions itself as a significant contribution to the field of machine learning.