人工智能
计算机科学
过度拟合
自编码
模式识别(心理学)
深度学习
平滑的
正规化(语言学)
机器学习
稀疏逼近
人工神经网络
计算机视觉
作者
Peng Yang,Zhen Wei,Qiong Yang,Xiaohua Xiao,Tianfu Wang,Baiying Lei,Ziwen Peng
标识
DOI:10.1016/j.eswa.2022.119389
摘要
Obsessive-compulsive disorder (OCD) brings many problems to patients. Redundant information in the OCD data can be removed to preserve valuable biological functions through sparse learning methods. Therefore, constructing a brain functional connectivity network (BFCN) with sparse learning is beneficial to objectively diagnose OCD. However, most studies ignore the relationship between subjects. Therefore, a new smooth sparse network (SSN) model is proposed to construct BFCN. Specifically, a smoothing term is designed in the objective function to capture the relationship between subjects. Then, a fused sparse auto-encoder (FSAE) model is proposed to learn the deep feature and decrease feature dimension of BFCN. The FASE is able to fuse the regularized sparse auto-encoder (RSAE) features and regularized stacking SAE (RSSAE) features for diagnosing OCD. Specifically, the l2-norm regularization is integrated in RSAE and RSSAE to address overfitting. Our proposed method combines the traditional machine learning with deep learning, which can achieve promising OCD diagnosis performance on our self- collected data.
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