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

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 network of networks approach for modeling interconnected brain tissue-specific networks

2019 ◽  
Vol 35 (17) ◽  
pp. 3092-3101 ◽  
Author(s):  
Hideko Kawakubo ◽  
Yusuke Matsui ◽  
Itaru Kushima ◽  
Norio Ozaki ◽  
Teppei Shimamura
Keyword(s):  
Learning Algorithm ◽  
Simulated Data ◽  
Autism Spectrum ◽  
Supplementary Information ◽  
Sparse Learning ◽  
Topological Information ◽  
Infinite Point ◽  
Neurogenetic Disorders ◽  
Information Matrices ◽  
Network Of Networks

Abstract Motivation Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. Results We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. Availability and implementation Our software is available from https://github.com/infinite-point/GOSPEL. Supplementary information Supplementary data are available at Bioinformatics online.

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

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

2015 ◽  
Vol 25 (14) ◽  
pp. 1540034 ◽  
Author(s):  
Wei Gao ◽  
Linli Zhu ◽  
Kaiyun Wang
Keyword(s):  
Physics Education ◽  
Learning Algorithm ◽  
Augmented Lagrangian Method ◽  
Ontology Mapping ◽  
Sparse Learning ◽  
Alternating Direction ◽  
Learning Techniques ◽  
Sparse Vector ◽  
Plant Ontology ◽  
Similarity Measuring

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

scholarly journals A Simultaneous Sparse Learning Algorithm of Structured Approximation with Transformation Analysis Embedded in Bayesian Framework

2021 ◽  
Author(s):  
Guisheng Wang
Keyword(s):  
Bayesian Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Approximation ◽  
Sparse Learning ◽  
Sparse Signals ◽  
Sparse Models ◽  
Wide Range ◽  
Feasible Solutions

<div>Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.</div>

Simultaneous sparse learning algorithm of structured approximation with transformation analysis embedded in Bayesian framework

2021 ◽  
Vol 30 (05) ◽  
Author(s):  
Guisheng Wang ◽  
Yequn Wang ◽  
Guoce Huang ◽  
Qinghua Ren ◽  
Tingting Ren
Keyword(s):  
Learning Algorithm ◽  
Bayesian Framework ◽  
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

scholarly journals Pathwise coordinate optimization for sparse learning: Algorithm and theory

2018 ◽  
Vol 46 (1) ◽  
pp. 180-218 ◽  
Author(s):  
Tuo Zhao ◽  
Han Liu ◽  
Tong Zhang
Keyword(s):  
Learning Algorithm ◽  
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.

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