Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data
暂无分享,去创建一个
Andrzej Cichocki | Shengli Xie | Tülay Adali | Qibin Zhao | Yu Zhang | Guoxu Zhou | A. Cichocki | T. Adalı | Yu Zhang | Guoxu Zhou | Qibin Zhao | S. Xie | Shengli Xie
[1] Donald Goldfarb,et al. Robust Low-Rank Tensor Recovery: Models and Algorithms , 2013, SIAM J. Matrix Anal. Appl..
[2] G. Golub,et al. A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies , 2007, Proceedings of the National Academy of Sciences.
[3] Eric F Lock,et al. JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES. , 2011, The annals of applied statistics.
[4] H. Hotelling. Relations Between Two Sets of Variates , 1936 .
[5] Vince D. Calhoun,et al. Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data , 2015, IEEE Transactions on Biomedical Engineering.
[6] Wei Wu,et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.
[7] Christian Jutten,et al. Joint blind source separation of multidimensional components: Model and algorithm , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).
[8] T. Adali,et al. Unmixing fMRI with independent component analysis , 2006, IEEE Engineering in Medicine and Biology Magazine.
[9] Chris H. Q. Ding,et al. Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.
[10] Michael W. Mahoney. Randomized Algorithms for Matrices and Data , 2011, Found. Trends Mach. Learn..
[11] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[12] Pierre Comon,et al. Nonnegative approximations of nonnegative tensors , 2009, ArXiv.
[13] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[14] Xiaofeng Gong,et al. Tensor decomposition of EEG signals: A brief review , 2015, Journal of Neuroscience Methods.
[15] Andrzej Cichocki,et al. Fast and unique Tucker decompositions via multiway blind source separation , 2012 .
[16] Jieping Ye,et al. Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Andreas Ziehe,et al. A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation , 2004, J. Mach. Learn. Res..
[18] Nikos D. Sidiropoulos,et al. Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200x , 2014, SDM.
[19] Rasmus Bro,et al. Multi-way Analysis with Applications in the Chemical Sciences , 2004 .
[20] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[21] Liqing Zhang,et al. Natural gradient algorithm for blind separation of overdetermined mixture with additive noise , 1999, IEEE Signal Processing Letters.
[22] Lars Kai Hansen,et al. ERPWAVELAB A toolbox for multi-channel analysis of time–frequency transformed event related potentials , 2007, Journal of Neuroscience Methods.
[23] Zenglin Xu,et al. Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis , 2011, ICML.
[24] Nikos D. Sidiropoulos,et al. Non-Negative Matrix Factorization Revisited: Uniqueness and Algorithm for Symmetric Decomposition , 2014, IEEE Transactions on Signal Processing.
[25] Guillermo Sapiro,et al. Dimensionality Reduction via Subspace and Submanifold Learning [From the Guest Editors] , 2011, IEEE Signal Process. Mag..
[26] David E. Booth,et al. Multi-Way Analysis: Applications in the Chemical Sciences , 2005, Technometrics.
[27] Pierre Comon,et al. Blind source separation of underdetermined mixtures of event-related sources , 2014, Signal Process..
[28] Andrzej Cichocki,et al. L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[29] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[30] Pierre Comon,et al. Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .
[31] Saeid Sanei,et al. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm , 2005, IEEE Signal Processing Letters.
[32] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective , 2014, IEEE Signal Processing Magazine.
[33] Jin Tang,et al. Graph-Laplacian PCA: Closed-Form Solution and Robustness , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Andrzej Cichocki,et al. Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions , 2014, ArXiv.
[35] Roman Orus,et al. A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States , 2013, 1306.2164.
[36] Chong-Yung Chi,et al. Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Andrzej Cichocki,et al. Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .
[38] Vince D. Calhoun,et al. Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties , 2015, Proceedings of the IEEE.
[39] Vince D. Calhoun,et al. Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA , 2014, Front. Syst. Neurosci..
[40] Andrzej Cichocki,et al. Common components analysis via linked blind source separation , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[41] F Wendling,et al. EEG extended source localization: Tensor-based vs. conventional methods , 2014, NeuroImage.
[42] Volkan Cevher,et al. Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics , 2014, IEEE Signal Processing Magazine.
[43] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorization T , 2007 .
[44] Trac D. Tran,et al. Tensor sparsification via a bound on the spectral norm of random tensors , 2010, ArXiv.
[45] Xingyu Wang,et al. Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..
[46] C. F. Beckmann,et al. Tensorial extensions of independent component analysis for multisubject FMRI analysis , 2005, NeuroImage.
[47] Saeid Sanei,et al. EEG signal processing , 2000, Clinical Neurophysiology.
[48] Gaojie Chen,et al. Independent vector analysis with a generalized multivariate Gaussian source prior for frequency domain blind source separation , 2014, Signal Process..
[49] Vince D. Calhoun,et al. Neuronal chronometry of target detection: Fusion of hemodynamic and event-related potential data , 2005, NeuroImage.
[50] Rasmus Bro,et al. Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.
[51] Hong Cheng,et al. Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion , 2014, NIPS.
[52] Hualiang Li,et al. Complex ICA Using Nonlinear Functions , 2008, IEEE Transactions on Signal Processing.
[53] Liqing Zhang,et al. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[54] Rasmus Bro,et al. Understanding data fusion within the framework of coupled matrix and tensor factorizations , 2013 .
[55] Nitish V. Thakor. IEEE Transactions on Neural Systems and Rehabilitation Engineering: Editorial , 2006 .
[56] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[57] V D Calhoun,et al. Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.
[58] Morten Mørup,et al. Non-negative Tensor Factorization with missing data for the modeling of gene expressions in the Human Brain , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
[59] Vince D. Calhoun,et al. Joint Blind Source Separation by Multiset Canonical Correlation Analysis , 2009, IEEE Transactions on Signal Processing.
[60] Guillermo Sapiro,et al. Dimensionality Reduction via Subspace and Submanifold Learning , 2011 .
[61] Vince D. Calhoun,et al. Canonical Correlation Analysis for Data Fusion and Group Inferences , 2010, IEEE Signal Processing Magazine.
[62] Andrzej Cichocki,et al. Equivariant Nonstationary Source Separation , 2002 .
[63] Nicolas Gillis,et al. Fast and Robust Recursive Algorithmsfor Separable Nonnegative Matrix Factorization , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[64] Anand Rangarajan,et al. Image Denoising Using the Higher Order Singular Value Decomposition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[65] W. Hackbusch. Tensor Spaces and Numerical Tensor Calculus , 2012, Springer Series in Computational Mathematics.
[66] Zhaoshui He,et al. Minimum-Volume-Constrained Nonnegative Matrix Factorization: Enhanced Ability of Learning Parts , 2011, IEEE Transactions on Neural Networks.
[67] Vince D. Calhoun,et al. Capturing group variability using IVA: A simulation study and graph-theoretical analysis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[68] Kyuwan Choi,et al. Detecting the Number of Clusters in n-Way Probabilistic Clustering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[69] Saeid Sanei,et al. EEG-FMRI integration using a partially constrained tensor factorization , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[70] Giorgio Ottaviani,et al. An Algorithm For Generic and Low-Rank Specific Identifiability of Complex Tensors , 2014, SIAM J. Matrix Anal. Appl..
[71] Andrzej Cichocki,et al. Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis , 2014, IEEE Signal Processing Magazine.
[72] N. Sidiropoulos,et al. On the uniqueness of multilinear decomposition of N‐way arrays , 2000 .
[73] Mark W. Woolrich,et al. Linked independent component analysis for multimodal data fusion , 2011, NeuroImage.
[74] Emmanuel J. Candès,et al. Matrix Completion With Noise , 2009, Proceedings of the IEEE.
[75] Ying Guo,et al. A unified framework for group independent component analysis for multi-subject fMRI data , 2008, NeuroImage.
[76] Florian Roemer,et al. Multi-dimensional space-time-frequency component analysis of event related EEG data using closed-form PARAFAC , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[77] T. Ens,et al. Blind signal separation : statistical principles , 1998 .
[78] Andrzej Cichocki,et al. Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation , 2012, IEEE Transactions on Signal Processing.
[79] Babak Hossein Khalaj,et al. A unified approach to sparse signal processing , 2009, EURASIP Journal on Advances in Signal Processing.
[80] Ronald Phlypo,et al. Independent Vector Analysis: Identification Conditions and Performance Bounds , 2013, IEEE Transactions on Signal Processing.
[81] Liqing Zhang,et al. Kernelization of Tensor-Based Models for Multiway Data Analysis: Processing of Multidimensional Structured Data , 2013, IEEE Signal Processing Magazine.
[82] Andrzej Cichocki,et al. Two Efficient Algorithms for Approximately Orthogonal Nonnegative Matrix Factorization , 2015, IEEE Signal Processing Letters.
[83] Xavier Bresson,et al. Robust Principal Component Analysis on Graphs , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[84] Vince D. Calhoun,et al. A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia , 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.
[85] Jason Farquhar,et al. Shared processing of perception and imagery of music in decomposed EEG , 2013, NeuroImage.
[86] Yang Song,et al. Determining the number of correlated signals between two data sets using PCA-CCA when sample support is extremely small , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[87] Joos Vandewalle,et al. A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..
[88] Tülay Adalı,et al. Diversity in Independent Component and Vector Analyses: Identifiability, algorithms, and applications in medical imaging , 2014, IEEE Signal Processing Magazine.
[89] Andrzej Cichocki,et al. Smooth PARAFAC Decomposition for Tensor Completion , 2015, IEEE Transactions on Signal Processing.
[90] Lieven De Lathauwer,et al. A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization , 2006, SIAM J. Matrix Anal. Appl..
[91] Sungjin Hong,et al. A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis , 2013, Journal of Neuroscience Methods.
[92] A. Cichocki,et al. Tensor decompositions for feature extraction and classification of high dimensional datasets , 2010 .
[93] Shengli Xie,et al. Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources , 2011, IEEE Transactions on Neural Networks.
[94] Lieven De Lathauwer,et al. Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures , 2007, IEEE Transactions on Signal Processing.
[95] Andrzej Cichocki,et al. Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems , 2014, ArXiv.
[96] Andrzej Cichocki,et al. Accelerated Canonical Polyadic Decomposition Using Mode Reduction , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[97] Tamara G. Kolda,et al. Scalable Tensor Factorizations for Incomplete Data , 2010, ArXiv.
[98] David B. Dunson,et al. Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors , 2014, ICML.
[99] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[100] J. Kettenring,et al. Canonical Analysis of Several Sets of Variables , 2022 .
[101] Demetri Terzopoulos,et al. Multilinear independent components analysis , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[102] Andrzej Cichocki,et al. Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[103] Andrzej Cichocki,et al. Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness , 2014, IEEE Transactions on Image Processing.
[104] Marko Filipovic,et al. Tucker factorization with missing data with application to low-$$n$$n-rank tensor completion , 2015, Multidimens. Syst. Signal Process..
[105] Wim Van Paesschen,et al. Block term decomposition for modelling epileptic seizures , 2014, EURASIP J. Adv. Signal Process..
[106] Pierre Comon,et al. Uniqueness of Nonnegative Tensor Approximations , 2014, IEEE Transactions on Information Theory.
[107] Liqing Zhang,et al. Bayesian Robust Tensor Factorization for Incomplete Multiway Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[108] Wim Van Paesschen,et al. Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study , 2007, Comput. Intell. Neurosci..
[109] Yukihiko Yamashita,et al. Smooth nonnegative matrix and tensor factorizations for robust multi-way data analysis , 2015, Signal Process..
[110] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[111] J. Pekar,et al. A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.
[112] Vince D. Calhoun,et al. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.
[113] P. Kroonenberg. Applied Multiway Data Analysis , 2008 .
[114] Andrzej Cichocki,et al. Canonical Polyadic Decomposition Based on a Single Mode Blind Source Separation , 2012, IEEE Signal Processing Letters.
[115] Jonathon A. Chambers,et al. Fetal electrocardiogram extraction by sequential source separation in the wavelet domain , 2005, IEEE Transactions on Biomedical Engineering.
[116] Lieven De Lathauwer,et al. Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization , 2013, SIAM J. Optim..
[117] Naotaka Fujii,et al. Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[118] Sabine Van Huffel,et al. A Combination of Parallel Factor and Independent Component Analysis , 2022 .
[119] Jun Zhang,et al. Nonorthogonal Approximate Joint Diagonalization With Well-Conditioned Diagonalizers , 2009, IEEE Transactions on Neural Networks.
[120] A. Cichocki,et al. Generalizing the column–row matrix decomposition to multi-way arrays , 2010 .
[121] Liqing Zhang,et al. Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion , 2015, ArXiv.
[122] Eric Moulines,et al. A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..
[123] V. Calhoun,et al. Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery , 2012, IEEE Reviews in Biomedical Engineering.
[124] Nicolas Gillis,et al. Sparse and unique nonnegative matrix factorization through data preprocessing , 2012, J. Mach. Learn. Res..
[125] François-Benoît Vialatte,et al. Independent vector analysis for SSVEP signal enhancement , 2015, 2015 49th Annual Conference on Information Sciences and Systems (CISS).
[126] Laurent Albera,et al. Fourth-order blind identification of underdetermined mixtures of sources (FOBIUM) , 2005, IEEE Transactions on Signal Processing.
[127] Xinyuan Zhang,et al. Denoising of 3D magnetic resonance images by using higher-order singular value decomposition , 2015, Medical Image Anal..
[128] Vince D. Calhoun,et al. Multidataset independent subspace analysis extends independent vector analysis , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[129] Tülay Adalı,et al. Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA , 2015, Journal of Neuroscience Methods.
[130] Sheng Luo,et al. Population Value Decomposition, a Framework for the Analysis of Image Populations , 2011, Journal of the American Statistical Association.