The study of healthy brain development helps to better understand both brain transformation and connectivity patterns, which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity measures of three datasets, derived from resting state functional magnetic resonance imaging (rs-fMRI) and two task fMRI data including a working memory n-back task (nb-fMRI) and an emotion identification task (em-fMRI). The fMRI data are collected from the Philadelphia Neurodevelopmental Cohort (PNC) for the prediction of brain age in adolescents. Due to extremely large variable-to-instance ratio of PNC data, a high-dimensional matrix with several irrelevant and highly correlated features is generated, and hence a sparse learning approach is necessary to extract effective features from fMRI data. We propose a sparse learner based on the residual errors along the estimation of an inverse problem for extreme learning machine (ELM). Our proposed method is able to overcome the overlearning problem by pruning several redundant features and their corresponding output weights. The proposed multimodal sparse ELM classifier based on residual errors is highly competitive in terms of classification accuracy compared to its counterparts such as conventional ELM, and sparse Bayesian learning ELM.

中文翻译：

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用于青少年大脑年龄预测的多模态稀疏分类器

对健康大脑发育的研究有助于更好地了解儿童期到成年期间发生的大脑转变和连接模式。本研究提出了一种稀疏机器学习解决方案，涵盖三个数据集的全脑功能连接测量，这些数据集源自静息态功能磁共振成像 (rs-fMRI) 和两个任务 fMRI 数据，包括工作记忆 n-back 任务 (nb-fMRI)和情绪识别任务（em-fMRI）。fMRI 数据收集自费城神经发育队列 (PNC)，用于预测青少年的大脑年龄。由于PNC数据的变量实例比极大，生成了具有多个不相关和高度相关特征的高维矩阵，因此，需要采用稀疏学习方法从功能磁共振成像数据中提取有效特征。我们提出了一种基于极限学习机（ELM）逆问题估计的残差的稀疏学习器。我们提出的方法能够通过修剪几个冗余特征及其相应的输出权重来克服过度学习问题。与传统ELM和稀疏贝叶斯学习ELM等同类产品相比，所提出的基于残差的多模态稀疏ELM分类器在分类精度方面具有很强的竞争力。我们提出的方法能够通过修剪几个冗余特征及其相应的输出权重来克服过度学习问题。与传统ELM和稀疏贝叶斯学习ELM等同类产品相比，所提出的基于残差的多模态稀疏ELM分类器在分类精度方面具有很强的竞争力。我们提出的方法能够通过修剪几个冗余特征及其相应的输出权重来克服过度学习问题。与传统ELM和稀疏贝叶斯学习ELM等同类产品相比，所提出的基于残差的多模态稀疏ELM分类器在分类精度方面具有很强的竞争力。