Medical prediction is always collectively determined based on bioimages collected from different sources or various clinical characterizations described from multiple physiological features. Notably, learning intrinsic structures from multiple heterogeneous features is significant but challenging in multi-view disease understanding. Different from existing methods that separately deal with each single view, this paper proposes a discriminative Margin-Sensitive Autoencoder (MSAE) framework for automated Alzheimer's disease (AD) diagnosis and accurate protein fold recognition. Generally, our MSAE aims to collaboratively explore the complementary properties of multi-view bioimage features in a semantic-sensitive encoder-decoder paradigm, where the discriminative semantic space is explicitly constructed in a margin-scalable regression model. Specifically, we develop a semantic-sensitive autoencoder, where an encoder projects multi-view visual features into the common semantic-aware latent space, and a decoder is exerted as an additional constraint to reconstruct the respective visual features. In particular, the importance of different views is adaptively weighted by self-adjusting learning scheme, such that their underlying correlations and complementary characteristics across multiple views are simultaneously preserved into the latent common representations. Moreover, a flexible semantic space is formulated by a margin-scalable support vector machine to improve the discriminability of the learning model. Importantly, correntropy induced metric is exploited as a robust regularization measurement to better control outliers for effective classification. A half-quadratic minimization and alternating learning strategy are devised to optimize the resulting framework such that each subproblem exists a closed-form solution in each iterative minimization phase. Extensive experimental results performed on the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets show that our MSAE can achieve superior performances for both binary and multi-class classification in AD diagnosis, and evaluations on protein folds demonstrate that our method can achieve very encouraging performance on protein structure recognition, outperforming the state-of-the-art methods.

中文翻译：

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区分余量敏感型自动编码器，用于集体多视点疾病分析。

始终根据从不同来源收集的生物图像或从多种生理特征描述的各种临床特征来共同确定医学预测。值得注意的是，从多种异质性特征中学习内在结构是重要的，但在多视角疾病理解中具有挑战性。与分别处理每个单一视图的现有方法不同，本文提出了一种区分性的边缘敏感自动编码器（MSAE）框架，用于自动诊断阿尔茨海默氏病（AD）和准确的蛋白质折叠识别。通常，我们的MSAE旨在在语义敏感的编码器/解码器范式中协作探索多视图生物图像特征的互补属性，其中在可扩展性的可伸缩回归模型中显式构造区分性语义空间。具体来说，我们开发了一种语义敏感的自动编码器，其中编码器将多视图视觉特征投影到公共的语义感知潜在空间中，并且解码器被施加​​为重构各个视觉特征的附加约束。特别地，通过自调整学习方案来自适应地加权不同视图的重要性，以使得它们在多个视图之间的潜在相关性和互补特性同时保留在潜在的公共表示中。此外，由边际可缩放支持向量机制定了灵活的语义空间，以提高学习模型的可分辨性。重要的是，将熵诱导的度量标准用作稳健的正则化度量，以更好地控制离群值以进行有效分类。设计了半二次最小化和交替学习策略来优化所得框架，以使每个子问题在每个迭代最小化阶段都存在一个封闭形式的解决方案。在阿尔茨海默氏病神经影像学倡议（ADNI）数据集上进行的大量实验结果表明，我们的MSAE可以在AD诊断中针对二元分类和多类分类均取得优异的性能，对蛋白质折叠的评估表明我们的方法可以对蛋白质取得非常令人鼓舞的性能结构识别，胜过最先进的方法。