ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease

Multimodal classification methods using different modalities of imaging and non-imaging data have great advantages over traditional single-modality-based ones for the diagnosis and prognosis of Alzheimer's disease (AD), as well as mild cognitive impairment (MCI) which is the prodromal stage of AD. With the increasing amount of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become a crucial research direction in medical image analysis. However, traditional methods usually depict the data structure using fixed and predefined similarity matrix as a priori, which is difficult to precisely measure the intrinsic relationship structure across different modalities in highdimensional spaces. In addition, based on the predefined similarity matrix, the chosen neighbors are suboptimal thus limiting the performance of the subsequent classification task. To overcome these drawbacks, in this paper, we propose a novel multi-modal feature selection method called Adaptive-Similarity-based Multi-modality Feature Selection (ASMFS) which performs adaptive similarity learning and feature selection simultaneously.