Neuroimaging Retrieval via Adaptive Ensemble Manifold Learning for Brain Disease Diagnosis.

Abstract:

:Alzheimer's disease (AD) is a neurodegenerative and non-curable disease, with serious cognitive impairment, such as dementia. Clinically, it is critical to study the disease with multi-source data in order to capture a global picture of it. In this respect, an adaptive ensemble manifold learning (AEML) algorithm is proposed to retrieve multi-source neuroimaging data. Specifically, an objective function based on manifold learning is formulated to impose geometrical constraints by similarity learning. The complementary characteristics of various sources of brain disease data for disorder discovery are investigated by tuning weights from ensemble learning. In addition, a generalized norm is explicitly explored for adaptive sparseness degree control. The proposed AEML algorithm is evaluated by the public AD neuroimaging initiative database. Results obtained from the extensive experiments demonstrate that our algorithm outperforms the traditional methods.

authors

Lei B,Yang P,Zhuo Y,Zhou F,Ni D,Chen S,Xiao X,Wang T

doi

10.1109/JBHI.2018.2872581

subject

Has Abstract

pub_date

2019-07-01 00:00:00

pages

1661-1673

issue

4

eissn

2168-2194

issn

2168-2208

journal_volume

23

pub_type

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