Multi-task learning has been widely applied to Alzheimer’s Disease (AD) studies due to its capability of simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this article, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for the label and regression score of the disease data under joint classification and regression tasks, while learning an adaptive shared similarity measure and corresponding feature mapping among these two tasks. We encode both the reconstructed label representation and regression score adaptive to the ideal similarity measure on disease data to achieve the ideal performance on these two joint tasks. The alternating algorithm is proposed to optimize the above objective. We theoretically prove the convergence of the optimization algorithm. The superiority of AMDSL is experimentally validated under joint classification and regression as per various evaluation metrics against the most authoritative Alzheimer’s disease data.

由于多任务学习能够同时对疾病的严重程度（分类）和预测相应的临床评分（回归）进行评估，因此已广泛应用于阿尔茨海默氏病（AD）研究。在本文中，我们通过学习联合分类和回归任务下疾病数据的标签和回归得分的双重流形结构，同时学习自适应方法，提出了一种自适应多任务双重结构学习的新技术，称为AMDSL。这两个任务之间共享相似性度量和相应的特征映射。我们对适应于疾病数据的理想相似性度量的重构标签表示和回归评分进行编码，以在这两个联合任务上实现理想性能。提出了交替算法来优化上述目标。我们从理论上证明了优化算法的收敛性。AMDSL的优越性已根据各种评估指标针对最权威的阿尔茨海默氏病数据进行了联合分类和回归实验验证。