论文标题
ASMF：基于适应性相似性的多模式特征选择，用于分类阿尔茨海默氏病
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
论文作者
论文摘要
随着要处理的高维异质数据的增加，多模式特征选择已成为医学图像分析中的重要研究方向。传统方法通常使用固定和预定义的相似性矩阵分别描述数据结构，而无需考虑不同方式的潜在关系结构。在本文中，我们提出了一种新型的多模式特征选择方法，该方法可以同时执行特征选择和局部相似性学习。特别是，通过共同考虑不同的成像方式来学习相似性矩阵。同时，通过施加稀疏的l_ {2，1}标准约束来进行特征选择。我们提出的联合学习方法的有效性可以通过对阿尔茨海默氏病神经影像倡议（ADNI）数据集的实验结果很好地证明，这表现优于现有的最先进的多模式方法。
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis. Traditional methods usually depict the data structure using fixed and predefined similarity matrix for each modality separately, without considering the potential relationship structure across different modalities. In this paper, we propose a novel multi-modality feature selection method, which performs feature selection and local similarity learning simultaniously. Specially, a similarity matrix is learned by jointly considering different imaging modalities. And at the same time, feature selection is conducted by imposing sparse l_{2, 1} norm constraint. The effectiveness of our proposed joint learning method can be well demonstrated by the experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which outperforms existing the state-of-the-art multi-modality approaches.
