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A review of heterogeneous data mining for brain disorder identification
2015
Brain Informatics
Preserved Fulltext
They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. ...
Brain disorder data poses many unique challenges for data mining research. ...
In this paper, we review some recent data mining methods for (1) mining tensor imaging data; (2) mining brain networks; and (3) mining multi-view feature vectors. ...
doi:10.1007/s40708-015-0021-3
pmid:27747561
pmcid:PMC4883173
fatcat:rhvqh4vmeffnnoxts7esxwxlsq
A review of heterogeneous data mining for brain disorders
[article]
2015
arXiv
pre-print
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They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis. ...
Brain disorder data poses many unique challenges for data mining research. ...
In this paper, we review some recent data mining methods for (1) mining tensor imaging data; (2) mining brain networks; (3) mining multiview feature vectors. ...
arXiv:1508.01023v1
fatcat:e6nscurzmbc23f26p2q42ccbrm
Brain Imaging Genomics: Integrated Analysis and Machine Learning
2019
Proceedings of the IEEE
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Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed ...
and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications. ...
[14] reviewed regression and correlation methods for brain imaging genomics as well as set-based methods for mining high-level imaging genomics associations. Mufford et al. ...
doi:10.1109/jproc.2019.2947272
pmid:31902950
pmcid:PMC6941751
fatcat:rx5b44yv55d2xicdiznnwjdac4
Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling
2016
IEEE Journal on Selected Topics in Signal Processing
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In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. ...
Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model Personal use is permitted, but republication/redistribution requires IEEE permission ...
This view aligns with prior work coined as joint BSS [37] , and even more so with multimodal brain data analysis. ...
doi:10.1109/jstsp.2016.2594945
pmid:28461840
pmcid:PMC5409135
fatcat:yd3brf4lsfd5tmgzgkxqpad4t4
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
2016
Foundations and Trends® in Machine Learning
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(EVD), PCA/SVD, huge systems of linear equations, pseudo-inverse of very large matrices, Lasso and Canonical Correlation Analysis (CCA) (This is Part 1) ...
It is therefore timely and valuable for the multidisciplinary research community to review tensor decompositions and tensor networks as emerging tools for large-scale data analysis and data mining. ...
Tensor networks can be thought of as special
graph structures which break down high-order tensors into a set of
sparsely interconnected low-order core tensors, thus allowing for both
enhanced interpretation ...
doi:10.1561/2200000059
fatcat:ememscddezeovamsoqrcpp33z4
Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia
2018
IEEE Transactions on Medical Imaging
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Here we proposed a fusion with reference model, called "multi-site canonical correlation analysis with reference plus joint independent component analysis" (MCCAR+jICA), which can precisely identify co-varying ...
Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases. ...
Acknowledgments This work was supported in part by NIH via a COBRE grant P20GM103472 and grants R01EB005846 and 1R01EB006841; the "100 Talents Plan" of the Chinese Academy of Sciences, the Chinese National ...
doi:10.1109/tmi.2017.2725306
pmid:28708547
pmcid:PMC5750081
fatcat:37lnuob67ncbfmjuf336oedszy
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 5324-5335 High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI. ...
., +, TIP 2020 9032-9043 High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Evolving Signal Processing for Brain–Computer Interfaces
2012
Proceedings of the IEEE
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Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral ...
| Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes ...
and tensor factorization [95] , multiview clustering and canonical correlation analysis [217] - [219] , metaclassification approaches [220] , [221] , and hierarchical Bayesian models and deep learning ...
doi:10.1109/jproc.2012.2185009
fatcat:ebed3ribeneptnatoxaoyzn4xm
Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data
2017
NeuroImage
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There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. ...
The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. ...
Acknowledgments This work was partially supported by the National High-Tech Development Plan (863) 2015AA020513, the "100 Talents Plan" of the Chinese Academy of Sciences (to J. ...
doi:10.1016/j.neuroimage.2016.05.026
pmid:27177764
pmcid:PMC5104674
fatcat:p74xh4imafah7mkncqscvatmma
2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25
2014
IEEE Transactions on Neural Networks and Learning Systems
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Yuan, M., +, TNNLS Nov. 2014 2119-2126 Image coding Efficient Kernel Sparse Coding Via First-Order Smooth Optimization. ...
Lee, J.H., +, TNNLS Dec. 2014 2250-2263 Visual databases Multilinear Sparse Principal Component Analysis. Lai, Z., +, TNNLS Oct. 2014 1942-1950 Sparse Alignment for Robust Tensor Learning. ...
doi:10.1109/tnnls.2015.2396731
fatcat:ztnfcozrejhhfdwg7t2f5xlype
End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations
2019
Conference on Uncertainty in Artificial Intelligence
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Our method is built around probabilistic canonical correlation analysis (PCCA), which is fit to image embeddings that are learned using convolutional neural networks and linear embeddings of paired gene ...
To understand this relationship, we develop a multimodal modeling and inference framework that estimates shared latent structure of joint gene expression levels and medical image features. ...
CANONICAL CORRELATION ANALYSIS Consider two paired data views, Y a ∈ R n×p a and Y b ∈ R n×p b . We assume the data are mean-centered. ...
dblp:conf/uai/GundersenDAE19
fatcat:zqbyocf73fep3diipyasy46eja
Visualization in Connectomics
[chapter]
2014
Mathematics and Visualization
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We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks. ...
Pfister et al. information from image data at macro-, meso-and microscales. ...
As a consequence, the analysis of the connectivity between single neurons has been limited to sparse analysis of statistical properties such as average synapse densities in different brain regions [20 ...
doi:10.1007/978-1-4471-6497-5_21
fatcat:hlsaktgoofabnaa25zpykhlgpa
Mining EEG–fMRI using independent component analysis
2009
International Journal of Psychophysiology
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Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. ...
An optimized method for symmetric EEG-fMRI decomposition is proposed and the outstanding challenges in multimodal integration are discussed. ...
Other options include canonical correlation analysis (CCA) in order to treat the problem. Here, however, 2nd level inference is less well defined. ...
doi:10.1016/j.ijpsycho.2008.12.018
pmid:19223007
pmcid:PMC2693483
fatcat:y6a3uh7abbawpdcgmv7yzdjwka
Visualization in Connectomics
[article]
2012
arXiv
pre-print
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Such a representation is believed to increase our understanding of how functional brain states emerge from their underlying anatomical structure. ...
In this paper, we review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe some remaining challenges. ...
As a consequence the analysis of the connectivity between single neurons has been limited to sparse analysis of statistical properties such as average synapse densities in different brain regions [21] ...
arXiv:1206.1428v2
fatcat:xjayhfspffe7hloxan4drfkggy
2021 Index IEEE Signal Processing Letters Vol. 28
2021
IEEE Signal Processing Letters
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., +, LSP 2021 2073-2077 Feature Fusion for Multimodal Emotion Recognition Based on Deep Canonical Correlation Analysis. ...
., +, LSP 2021 2038-2042 Feature Fusion for Multimodal Emotion Recognition Based on Deep Canon-ical Correlation Analysis. ...
+ Check author entry for coauthors
H Handwriting recognition Handwritten Text Generation via Disentangled Representations. ...
doi:10.1109/lsp.2022.3145253
fatcat:a3xqvok75vgepcckwnhh2mty74
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