  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.
