数字农科院2.0

Self-paced semi-supervised feature selection with application to multi-modal Alzheimer's disease classification

文献类型： 外文期刊

作者： Chao Zhang;Wentao Fan;Bo Wang;Chunlin Chen;Huaxiong Li

作者机构：

关键词： Alzheimer's disease;Multi-modal learning;Self-paced learning;Semi-supervised learning

期刊名称： Information Fusion

ISSN： 1566-2535

年卷期： 2024 年 107 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： 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.

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