Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration

[1]  Nassir Navab,et al.  Pattern Visualization and Recognition Using Tensor Factorization for Early Differential Diagnosis of Parkinsonism , 2017, MICCAI.

[2]  S. Van Huffel,et al.  Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas , 2017, IEEE Journal of Biomedical and Health Informatics.

[3]  G. Tutz,et al.  Random forests for functional covariates , 2016 .

[4]  R. T. Smith,et al.  HYPERSPECTRAL AUTOFLUORESCENCE IMAGING OF DRUSEN AND RETINAL PIGMENT EPITHELIUM IN DONOR EYES WITH AGE-RELATED MACULAR DEGENERATION. , 2016, Retina.

[5]  R. T. Smith,et al.  Spatial and Spectral Characterization of Human Retinal Pigment Epithelium Fluorophore Families by Ex Vivo Hyperspectral Autofluorescence Imaging , 2016, Translational vision science & technology.

[6]  Ana-Maria Staicu,et al.  A two‐sample distribution‐free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis , 2016, Journal of the Royal Statistical Society. Series C, Applied statistics.

[7]  Stéphane Marcet,et al.  Hyperspectral Microscopy of Near-Infrared Fluorescence Enables 17-Chirality Carbon Nanotube Imaging , 2015, Scientific Reports.

[8]  Xiaofeng Gong,et al.  Tensor decomposition of EEG signals: A brief review , 2015, Journal of Neuroscience Methods.

[9]  Nikos D. Sidiropoulos,et al.  A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization , 2015, IEEE Transactions on Signal Processing.

[10]  Liang Gao,et al.  Optical hyperspectral imaging in microscopy and spectroscopy – a review of data acquisition , 2015, Journal of biophotonics.

[11]  Alix Le Marois,et al.  Fluorescence lifetime imaging (FLIM): Basic concepts and some recent developments , 2015 .

[12]  July Galeano,et al.  Blind source separation of ex-vivo aorta tissue multispectral images. , 2015, Biomedical optics express.

[13]  R. T. Smith,et al.  Simultaneous decomposition of multiple hyperspectral data sets: signal recovery of unknown fluorophores in the retinal pigment epithelium. , 2014, Biomedical optics express.

[14]  G. Wetzstein,et al.  Attenuation-corrected fluorescence spectra unmixing for spectroscopy and microscopy. , 2014, Optics express.

[15]  Danielle B. Gutierrez,et al.  Quantitative autofluorescence and cell density maps of the human retinal pigment epithelium. , 2014, Investigative ophthalmology & visual science.

[16]  Misha Elena Kilmer,et al.  Novel Methods for Multilinear Data Completion and De-noising Based on Tensor-SVD , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Paul D. Gader,et al.  A Review of Nonlinear Hyperspectral Unmixing Methods , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  C. Curcio,et al.  Mathematical modeling of retinal pigment epithelium (RPE) autofluorescence (AF) with Gaussian mixture models and non-negative matrix factorization (NMF) , 2014 .

[19]  Andrzej Cichocki,et al.  Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis , 2014, IEEE Signal Processing Magazine.

[20]  R. Klein,et al.  Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. , 2014, The Lancet. Global health.

[21]  David M. Jameson,et al.  Introduction to Fluorescence , 2014 .

[22]  Gretchen A. Stevens,et al.  Causes of vision loss worldwide, 1990-2010: a systematic analysis. , 2013, The Lancet. Global health.

[23]  Miguel Á. Carreira-Perpiñán,et al.  Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application , 2013, ArXiv.

[24]  Lei Huang,et al.  Core consistency diagnostic aided by reconstruction error for accurate enumeration of the number of components in parafac models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  Richard M Caprioli,et al.  Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. , 2013, Chemical reviews.

[26]  C. Curcio,et al.  SUBRETINAL DRUSENOID DEPOSITS IN NON-NEOVASCULAR AGE-RELATED MACULAR DEGENERATION: Morphology, Prevalence, Topography, and Biogenesis Model , 2013, Retina.

[27]  Anima Anandkumar,et al.  Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..

[28]  Martin Styner,et al.  Quantitative tract-based white matter development from birth to age 2years , 2012, NeuroImage.

[29]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Jean-Marc Dinten,et al.  In Vivo Fluorescence Spectra Unmixing and Autofluorescence Removal by Sparse Nonnegative Matrix Factorization , 2011, IEEE Transactions on Biomedical Engineering.

[31]  Martin Styner,et al.  FADTTS: Functional analysis of diffusion tensor tract statistics , 2011, NeuroImage.

[32]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[33]  Xiaodong Tao,et al.  Autofluorescence Removal by Non-Negative Matrix Factorization , 2011, IEEE Transactions on Image Processing.

[34]  R. Alfano,et al.  Native Fluorescence Spectroscopic Evaluation of Chemotherapeutic Effects on Malignant Cells using Nonnegative Matrix Factorization Analysis , 2011, Technology in cancer research & treatment.

[35]  Christopher J. Hillar,et al.  Most Tensor Problems Are NP-Hard , 2009, JACM.

[36]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[38]  L. K. Hansen,et al.  Automatic relevance determination for multi‐way models , 2009 .

[39]  Jennifer C. Waters,et al.  Accuracy and precision in quantitative fluorescence microscopy , 2009, The Journal of cell biology.

[40]  Binxing Li,et al.  Purification and partial characterization of a lutein-binding protein from human retina. , 2009, Biochemistry.

[41]  Fabian J Theis,et al.  Blind source separation techniques for the decomposition of multiply labeled fluorescence images. , 2009, Biophysical journal.

[42]  Hans-Peter Seidel,et al.  Estimating Crossing Fibers: A Tensor Decomposition Approach , 2008, IEEE Transactions on Visualization and Computer Graphics.

[43]  Yoram Singer,et al.  Efficient projections onto the l1-ball for learning in high dimensions , 2008, ICML '08.

[44]  W. Saeys,et al.  Potential applications of functional data analysis in chemometrics , 2008 .

[45]  Hyunsoo Kim,et al.  Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method , 2008, SIAM J. Matrix Anal. Appl..

[46]  Chih-Jen Lin,et al.  On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization , 2007, IEEE Transactions on Neural Networks.

[47]  Pieter M. Kroonenberg,et al.  Missing Data in Multiway Analysis , 2007 .

[48]  P. Hall,et al.  Properties of principal component methods for functional and longitudinal data analysis , 2006, math/0608022.

[49]  T. Kolda.  Multilinear operators for higher-order decompositions , 2006 .

[50]  M. Boulton,et al.  RPE lipofuscin and its role in retinal pathobiology. , 2005, Experimental eye research.

[51]  R. Bro,et al.  PARAFAC and missing values , 2005 .

[52]  Rasmus Bro,et al.  Multi-way Analysis with Applications in the Chemical Sciences , 2004 .

[53]  Régis Huez,et al.  Application of Non-negative Matrix Factorization to fluorescence spectroscopy , 2004, 2004 12th European Signal Processing Conference.

[54]  Lucas C. Parra,et al.  Recovery of constituent spectra using non-negative matrix factorization , 2003, SPIE Optics + Photonics.

[55]  H. Müller,et al.  Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate , 2003, Biometrics.

[56]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2003, IEEE Transactions on Geoscience and Remote Sensing.

[57]  R. Bro,et al.  A new efficient method for determining the number of components in PARAFAC models , 2003 .

[58]  James O. Ramsay,et al.  Applied Functional Data Analysis: Methods and Case Studies , 2002 .

[59]  Rasmus Bro,et al.  The N-way Toolbox for MATLAB , 2000 .

[60]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[61]  H. Kiers.  Weighted least squares fitting using ordinary least squares algorithms , 1997 .

[62]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[63]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[64]  J. Kruskal.  Rank, decomposition, and uniqueness for 3-way and n -way arrays , 1989 .

[65]  Johan Håstad,et al.  Tensor Rank is NP-Complete , 1989, ICALP.

[66]  M. Stephens,et al.  K-Sample Anderson–Darling Tests , 1987 .

[67]  M. Stephens.  EDF Statistics for Goodness of Fit and Some Comparisons , 1974 .

[68]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[69]  F. L. Hitchcock.  The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .

[70]  Nicolas Dobigeon,et al.  Linear and nonlinear unmixing in hyperspectral imaging , 2016 .

[71]  Y. Koutalos,et al.  A2E and Lipofuscin. , 2015, Progress in Molecular Biology and Translational Science.

[72]  Seungjin Choi,et al.  Semi-Supervised Nonnegative Matrix Factorization , 2010, IEEE Signal Processing Letters.

[73]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[74]  Ian J Constable,et al.  Lipofuscin of the retinal pigment epithelium: A review , 1995, Eye.

[75]  H. Ahrens.  Coppi, R., S. Bolasco (Eds.): Multiway Data Analysis. North‐Holland, Amsterdam 1989, xiv+552 S., US‐$ 136.75; Dfl. 260.‐, ISBN 0 444 87410 0 , 1991 .

[76]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[77]  Michael Kasha,et al.  Characterization of electronic transitions in complex molecules , 1950 .

[78]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .