Semi-supervised multi-modal learning has attracted much attention due to the expense and scarcity of data labels, especially in disease diagnosis field. Most existing methods follow the paradigm by iteratively inferring the pseudo-labels of unlabeled data and add them into training sequence, but they ignore the reliability of those pseudo-labels, where inaccurate and wrong supervision will lead to negative influence on model learning. In this paper, we propose a Self-paced Semi-supervised Multi-modal Feature Selection (SSMFS) method, and apply it to Alzheimer’s disease classification. Specifically, SSMFS projects multi-modal biomedical data into the common label space with discriminative feature selection. Under the guidance of prior multi-modal similarity graphs, a unified graph is adaptively learned and embedded to preserve the neighborhood structures. More importantly, SSMFS dynamically investigates the discriminability and credibility of pseudo-labels, and adaptively assigns a weight to each unlabeled sample via self-paced learning such that the negative influence of wrong supervision can be reduced. Finally, a multi-kernel support vector machine is used to fuse the selected multi-modal features for final disease prediction. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our method.

中文翻译：

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自定进度的半监督特征选择及其在多模式阿尔茨海默病分类中的应用

半监督多模态学习由于数据标签的昂贵和稀缺而备受关注，特别是在疾病诊断领域。大多数现有方法遵循范式，通过迭代推断未标记数据的伪标签并将其添加到训练序列中，但它们忽略了这些伪标签的可靠性，其中不准确和错误的监督会对模型学习产生负面影响。在本文中，我们提出了一种自定进度半监督多模态特征选择（SSMFS）方法，并将其应用于阿尔茨海默病分类。具体来说，SSMFS 通过判别性特征选择将多模态生物医学数据投影到公共标签空间中。在先前的多模态相似图的指导下，自适应地学习和嵌入统一图以保留邻域结构。更重要的是，SSMFS动态地研究伪标签的可辨别性和可信度，并通过自定进度学习自适应地为每个未标记样本分配权重，从而减少错误监督的负面影响。最后，使用多核支持向量机融合所选的多模态特征以进行最终的疾病预测。阿尔茨海默病神经影像计划（ADNI）数据集的实验结果证明了我们方法的有效性。