Discriminative margin-sensitive autoencoder for collective multi-view disease analysis

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.