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In recent years, we have witnessed the explosion of large-scale data in various fields. Classical statistical methodologies, such as linear regression or generalized linear regression, often show inadequate performance on heterogeneous data because the key homogeneity assumption fails. In this paper, we present a flexible framework to handle heterogeneous populations that can be naturally grouped into several ordered subtypes. A local model technique utilizing ordinal class labels during the training stage is proposed. We define a new “progression score” that captures the progression of ordinal classes, and use a truncated Gaussian kernel to construct the weight function in a local regression framework. Furthermore, given the weights, we apply sparse shrinkage on the local fitting to handle high dimensionality. In this way, our local model is able to conduct variable selection on each query point. Numerical studies show the superiority of our proposed method over several existing ones. Our method is also applied to the Alzheimer’s Disease Neuroimaging Initiative data to make predictions on the longitudinal clinical scores based on different modalities of baseline brain image features. [ABSTRACT FROM AUTHOR]

As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification. [ABSTRACT FROM AUTHOR]