Due to its wide applications and learning efficiency, online ordinal regression using perceptron algorithms with interval labels (PRIL) has been increasingly applied to solve ordinal ranking problems. However, it is still a challenge for the PRIL method to handle noise labels, in which case the ranking results may change dramatically. To tackle this problem, in this paper, we propose noise-resilient online learning algorithms using ramp loss function, called PRIL-RAMP, and its nonlinear variant K-PRIL-RAMP, to improve the performance of PRIL method for noisy data streams. The proposed algorithms iteratively optimize the decision function under the framework of online gradient descent (OGD), and we justify the algorithms by showing the order preservation of thresholds. It is validated in the experiments that both approaches are more robust and efficient to noise labels than state-of-the-art online ordinal regression algorithms on real-world datasets.

中文翻译：

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使用具有斜坡损失的间隔标签的Perceptron排名进行在线有序回归

由于其广泛的应用和学习效率，使用带有间隔标签的感知器算法（PRIL）的在线序数回归已越来越多地应用于解决序数排名问题。但是，对于PRIL方法来说，处理噪声标签仍然是一个挑战，在这种情况下，排名结果可能会发生巨大变化。为了解决这个问题，在本文中，我们提出了一种使用斜坡损失函数的抗噪声在线学习算法，称为PRIL-RAMP及其非线性变体K-PRIL-RAMP，以提高PRIL方法在嘈杂数据流中的性能。提出的算法在在线梯度下降（OGD）框架下迭代优化决策函数，并通过显示阈值的顺序保持来证明算法的正确性。