scholarly journals Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis

2013 ◽  
Vol 12 (3) ◽  
pp. 381-394 ◽  
Author(s):  
Manhua Liu ◽  
◽  
Daoqiang Zhang ◽  
Dinggang Shen
Keyword(s):  
Imaging Biomarkers ◽  
Sparse Learning ◽  
Structured Sparse Learning

scholarly journals Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning

2012 ◽  
pp. 1-17 ◽  
Author(s):  
Jingwen Yan ◽  
Shannon L. Risacher ◽  
Sungeun Kim ◽  
Jacqueline C. Simon ◽  
Taiyong Li ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Sparse Learning ◽  
Multimodal Neuroimaging ◽  
Structured Sparse Learning

scholarly journals A systematic review of structured sparse learning

2017 ◽  
Vol 18 (4) ◽  
pp. 445-463 ◽  
Author(s):  
Lin-bo Qiao ◽  
Bo-feng Zhang ◽  
Jin-shu Su ◽  
Xi-cheng Lu
Keyword(s):  
Systematic Review ◽  
Sparse Learning ◽  
Structured Sparse Learning

Online structured sparse learning with labeled information for robust object tracking

2017 ◽  
Vol 26 (1) ◽  
pp. 013007 ◽  
Author(s):  
Baojie Fan ◽  
Yang Cong ◽  
Yandong Tang
Keyword(s):  
Object Tracking ◽  
Sparse Learning ◽  
Structured Sparse Learning

New Sparse Facial Feature Description Model Based on Salience Evaluation of Regions and Features

2015 ◽  
Vol 29 (05) ◽  
pp. 1556007 ◽  
Author(s):  
Yue Zhao ◽  
Jianbo Su
Keyword(s):  
Face Recognition ◽  
Evaluation Method ◽  
Facial Feature ◽  
Experimental Results ◽  
Sparse Learning ◽  
Feature Description ◽  
Model Based ◽  
Face Images ◽  
Proposed Model ◽  
Structured Sparse Learning

Some regions (or blocks) and their affiliated features of face images are normally of more importance for face recognition. However, the variety of feature contributions, which exerts different saliency on recognition, is usually ignored. This paper proposes a new sparse facial feature description model based on salience evaluation of regions and features, which not only considers the contributions of different face regions, but also distinguishes that of different features in the same region. Specifically, the structured sparse learning scheme is employed as the salience evaluation method to encourage sparsity at both the group and individual levels for balancing regions and features. Therefore, the new facial feature description model is obtained by combining the salience evaluation method with region-based features. Experimental results show that the proposed model achieves better performance with much lower feature dimensionality.

scholarly journals Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm

2014 ◽  
Vol 30 (17) ◽  
pp. i564-i571 ◽  
Author(s):  
J. Yan ◽  
L. Du ◽  
S. Kim ◽  
S. L. Risacher ◽  
H. Huang ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Learning Algorithm ◽  
Amyloid Imaging ◽  
Sparse Learning ◽  
Structured Sparse Learning

Hierarchical Structured Sparse Learning for Schizophrenia Identification

2019 ◽  
Vol 18 (1) ◽  
pp. 43-57
Author(s):  
Mingliang Wang ◽  
Xiaoke Hao ◽  
Jiashuang Huang ◽  
Kangcheng Wang ◽  
Li Shen ◽  
...  
Keyword(s):  
Sparse Learning ◽  
Structured Sparse Learning
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