基于广义稀疏逻辑回归的全脑分类

Whole-brain Classification Based on Generalized Sparse Logistic Regression

已有的稀疏逻辑回归算法不能充分利用大脑的空间结构信息,因此不适合直接应用于全脑fMRI数据分类。针对此问题,将表征稀疏性的惩罚项和表征空间结构的惩罚项同时引入逻辑回归算法中,提出了一类广义稀疏逻辑回归算法。具体而言,设计了一种灵活地表征空间结构的惩罚项。通过对该惩罚项调节参数,可以更加充分地利用大脑的空间结构信息。在优化最大化框架下,设计了一个迭代流程来求解对应的优化问题,保证每步迭代都有显式解,并且最终能得到该问题的局部最优解。实验结果表明,所提出的算法相对于已有的稀疏逻辑回归算法在分类准确率上具有明显优势。

Previously proposed sparse logistic regression algorithms cannot make the most of the spatial structure information of brain,so it is not appropriate to be applied directly in whole-brain fMRI data classification.To address the problem,two penalty terms of characterizing sparsity and spatial structure are simultaneously introduced into traditional logistic regression algorithm,which produces a generalized sparse logistic regression algorithm.Specifically,a flexible penalty term that characterizes the spatial structure is designed.By tuning parameters of this penalty term,the spatial structure information of brain can be fully utilized.Under the framework of minorization-maximization,an iterative process is designed to solve the corresponding optimization problem,which guarantees that an explicit solution can be obtained in each iteration and a locally optimal solution for the problem can be found.Experimental results show that the proposed algorithm outperforms existing sparse logistic regression algorithms in classification accuracy.