动态特征和噪声标签的自适应学习

Shilin Gu, Chao Xu, Dewen Hu, Chenping Hou
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摘要

在复杂和开放的环境中应用当前的机器学习算法仍然具有挑战性,尤其是当不同的变化元素耦合在一起且训练数据稀缺时。例如,在活动识别任务中,运动传感器可能会因活动强度而改变位置或脱落,从而导致特征空间发生变化,最后产生标签噪声。从这种动态特征与噪声标签耦合的问题中学习至关重要,但却鲜有研究,尤其是当新特征空间中的噪声样本有限时。本文针对上述问题,提出了一种新颖的两阶段算法,即动态特征和噪声标签自适应学习算法(ALDN)。具体来说,首先修改最优传输,将之前学习到的异构模型映射到当前阶段的先验模型。然后,为了充分利用映射的先验模型,我们添加了一个简单但高效的正则器作为一致性约束,以帮助当前阶段的噪声转换矩阵估计和模型训练。最后,为了更好地研究,我们展示了直接(ALDN-D)和间接(ALDN-ID)约束的两种实现方法。更重要的是,我们为 ALDN-D 和 ALDN-ID 的风险最小化提供了理论保证。大量实验验证了所提算法的有效性。
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Adaptive Learning for Dynamic Features and Noisy Labels.

Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is firstly modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms.

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