In real-world applications, perfect labels are rarely available, making it
challenging to develop robust machine learning algorithms that can handle noisy
labels. Recent methods have focused on filtering noise based on the discrepancy
between model predictions and given noisy labels, assuming that samples with
small classification losses are clean. This work takes a different approach by
leveraging the consistency between the learned model and the entire noisy
dataset using the rich representational and topological information in the
data. We introduce LaplaceConfidence, a method that to obtain label confidence
(i.e., clean probabilities) utilizing the Laplacian energy. Specifically, it
first constructs graphs based on the feature representations of all noisy
samples and minimizes the Laplacian energy to produce a low-energy graph. Clean
labels should fit well into the low-energy graph while noisy ones should not,
allowing our method to determine data's clean probabilities. Furthermore,
LaplaceConfidence is embedded into a holistic method for robust training, where
co-training technique generates unbiased label confidence and label
refurbishment technique better utilizes it. We also explore the dimensionality
reduction technique to accommodate our method on large-scale noisy datasets.
Our experiments demonstrate that LaplaceConfidence outperforms state-of-the-art
methods on benchmark datasets under both synthetic and real-world noise.

利用富有代表性和拓扑信息的数据，通过构建图形和最小化拉普拉斯能量来获取标签置信度，LaplaceConfidence 是一种处理有噪声标签的机器学习算法，并在维度缩减技术的支持下，在合成和实际噪声下优于现有方法。