A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data.
A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data.
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DOI：
10.1002/sam.10141
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发表时间：
2011-12-01
影响因子：
1.3
通讯作者：
Bowman, F DuBois
中科院分区：
文献类型：
--
作者：
Chen, Shuo;Bowman, F DuBois
最近的技术进步使得许多研究可以纵向收集高维数据（HDD），例如在不同扫描会话期间收集的图像。这些研究可以产生选定特征的时间变化，当与机器学习方法结合时，能够预测疾病状态或对治疗性治疗的反应。支持向量机（SVM）技术是一种鲁棒有效的工具，非常适合于HDD的分类和预测。然而，用于HDD分析的当前SVM方法通常考虑在一个时间段或会话（例如基线）期间收集的横截面数据。我们提出了一种新的支持向量分类器（SVC）的纵向HDD，允许同时估计的SVM分离超平面参数和时间趋势参数，确定最佳的手段来联合收割机的纵向数据进行分类和预测。我们的方法是基于一个增广再生核函数，并使用二次规划优化。我们使用模拟研究和阿尔茨海默病神经成像倡议的数据示例来展示我们提出的方法的用途和潜在优势。结果表明，我们提出的方法利用额外的纵向信息，以实现更高的精度比方法，仅使用横截面数据和方法，联合收割机纵向数据通过天真地扩大特征空间。
Recent technological advances have made it possible for many studies to collect high dimensional data (HDD) longitudinally, for example images collected during different scanning sessions. Such studies may yield temporal changes of selected features that, when incorporated with machine learning methods, are able to predict disease status or responses to a therapeutic treatment. Support vector machine (SVM) techniques are robust and effective tools well-suited for the classification and prediction of HDD. However, current SVM methods for HDD analysis typically consider cross-sectional data collected during one time period or session (e.g. baseline). We propose a novel support vector classifier (SVC) for longitudinal HDD that allows simultaneous estimation of the SVM separating hyperplane parameters and temporal trend parameters, which determine the optimal means to combine the longitudinal data for classification and prediction. Our approach is based on an augmented reproducing kernel function and uses quadratic programming for optimization. We demonstrate the use and potential advantages of our proposed methodology using a simulation study and a data example from the Alzheimer's disease Neuroimaging Initiative. The results indicate that our proposed method leverages the additional longitudinal information to achieve higher accuracy than methods using only cross-sectional data and methods that combine longitudinal data by naively expanding the feature space.