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

arXiv: https://arxiv.org/abs/2010.08190 · HTML: https://mlsanity.com/paper/2010.08190/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2403.03512/) (2024)
- [GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI](https://mlsanity.com/paper/2407.15719/) (2024)
- [Multi-Objective Genetic Algorithm for Multi-View Feature Selection](https://mlsanity.com/paper/2305.18352/) (2023)
- [Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal](https://mlsanity.com/paper/2308.02433/) (2023)
- [Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation](https://mlsanity.com/paper/2404.14696/) (2024)
- [A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis](https://mlsanity.com/paper/2502.01885/) (2025)
- [Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2508.06565/) (2025)
- [CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification](https://mlsanity.com/paper/2510.17584/) (2025)

## Abstract (arXiv)

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.