Automatically generated by Mendeley Desktop 1.16.3
Any changes to this file will be lost if it is regenerated by Mendeley.
BibTeX export options can be customized via Options -> BibTeX in Mendeley Desktop
@article{Li2007,
author = {Li, Hualiang and Adali, T{\"{u}}lay and Wang, Wei and Emge, Darren and Cichocki, Andrzej},
doi = {10.1007/s11265-006-0039-0},
issn = {0922-5773},
journal = {The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology},
month = {feb},
number = {1-2},
pages = {83--97},
title = {{Non-negative Matrix Factorization with Orthogonality Constraints and its Application to Raman Spectroscopy}},
url = {http://link.springer.com/10.1007/s11265-006-0039-0},
volume = {48},
year = {2007}
}
@inproceedings{Li2005,
author = {Li, Hualiang and Adali, T{\"{u}}lay and Wang, Wei and Emge, Darren},
booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
isbn = {0780395182},
pages = {253--258},
title = {{Non-negative matrix factorization with orthogonality constraints for chemical agent detection in raman spectra}},
year = {2005}
}
@article{Alstrom2014,
author = {Alstr{\o}m, Tommy S. and Fr{\o}hling, Kasper B. and Larsen, Jan and Schmidt, Mikkel N. and Bache, Michael and Schmidt, Michael S. and Jakobsen, Mogens H. and Boisen, Anja},
doi = {10.1109/MLSP.2014.6958925},
isbn = {978-1-4799-3694-6},
journal = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
pages = {1--6},
title = {{Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6958925},
year = {2014}
}
@article{Weatherston2016,
author = {Weatherston, Joshua D. and Worstell, Nolan C. and Wu, Hung-Jen},
doi = {10.1039/C6AN01098A},
issn = {0003-2654},
journal = {The Analyst},
title = {{Quantitative surface-enhanced Raman spectroscopy for kinetic analysis of aldol condensation using Ag–Au core–shell nanocubes}},
url = {http://xlink.rsc.org/?DOI=C6AN01098A},
year = {2016}
}
@book{Prochazka2016,
address = {Cham},
author = {Proch{\'{a}}zka, Marek},
booktitle = {Analytica Chimica Acta},
doi = {10.1007/978-3-319-23992-7},
isbn = {978-3-319-23990-3},
issn = {00032670},
month = {sep},
pages = {1--13},
publisher = {Springer International Publishing},
series = {Biological and Medical Physics, Biomedical Engineering},
title = {{Surface-Enhanced Raman Spectroscopy}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0003267015004754 http://link.springer.com/10.1007/978-3-319-23992-7},
volume = {893},
year = {2016}
}
@misc{serstech_com,
title = {Serstech},
howpublished = {http://www.serstech.com},
}
@article{Palla2015a,
author = {Palla, Mirk{\'{o}} and Bosco, Filippo G. and Yang, Jaeyoung and Rindzevicius, Tomas and Alstrom, Tommy S. and Schmidt, Michael S. and Lin, Qiao and Ju, Jingyue and Boisen, Anja},
doi = {10.1039/C5RA16108H},
issn = {2046-2069},
journal = {RSC Adv.},
number = {104},
pages = {85845--85853},
title = {{Mathematical model for biomolecular quantification using large-area surface-enhanced Raman spectroscopy mapping}},
url = {http://xlink.rsc.org/?DOI=C5RA16108H},
volume = {5},
year = {2015}
}
@article{YPBA13,
author = {Yang, JaeYoung and Palla, Mirko and Bosco, Filippo G. and Rindzevicius, Tomas and Alstr{\o}m, Tommy S. and Schmidt, Michael S. and Boisen, Anja and Ju, Jingyue and Lin, Qiao},
doi = {10.1021/nn401199k},
issn = {1936-0851},
journal = {ACS Nano},
month = {jun},
number = {6},
pages = {5350--5359},
title = {{Surface-Enhanced Raman Spectroscopy Based Quantitative Bioassay on Aptamer-Functionalized Nanopillars Using Large-Area Raman Mapping}},
volume = {7},
year = {2013}
}
@article{Sundius1973,
author = {Sundius, T.},
doi = {10.1002/jrs.1250010506},
issn = {03770486},
journal = {Journal of Raman Spectroscopy},
month = {nov},
number = {5},
pages = {471--488},
title = {{Computer fitting of Voigt profiles to Raman lines}},
url = {http://doi.wiley.com/10.1002/jrs.1250010506},
volume = {1},
year = {1973}
}
@article{Tauler1993,
author = {Tauler, Roma. and Kowalski, Bruce. and Fleming, Sydney.},
doi = {10.1021/ac00063a019},
issn = {0003-2700},
journal = {Analytical Chemistry},
month = {aug},
number = {15},
pages = {2040--2047},
title = {{Multivariate curve resolution applied to spectral data from multiple runs of an industrial process}},
url = {http://pubs.acs.org/doi/abs/10.1021/ac00063a019},
volume = {65},
year = {1993}
}
@article{Nie1997,
author = {Nie, Shuming and Emory, Steven R.},
doi = {10.1126/science.275.5303.1102},
issn = {00368075},
journal = {Science},
month = {feb},
number = {5303},
pages = {1102--1106},
title = {{Probing Single Molecules and Single Nanoparticles by Surface-Enhanced Raman Scattering}},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.275.5303.1102},
volume = {275},
year = {1997}
}
@article{Sajda2003,
abstract = {In this paper a constrained non-negative matrix factorization (cNMF) algorithm for recovering constituent spectra is described together with experiments demonstrating the broad utility of the approach. The algorithm is based on the NMF algorithm of Lee and Seung, extending it to include a constraint on the minimum amplitude of the recovered spectra. This constraint enables the algorithm to deal with observations having negative values by assuming they arise from the noise distribution. The cNMF algorithm does not explicitly enforce independence or sparsity, instead only requiring the source and mixing matrices to be non-negative. The algorithm is very fast compared to other "blind" methods for recovering spectra. cNMF can be viewed as a maximum likelihood approach for finding basis vectors in a bounded subspace. In this case the optimal basis vectors are the ones that envelope the observed data with a minimum deviation from the boundaries. Results for Raman spectral data, hyperspectral images, and human brain data are provided to illustrate the algorithm's performance.},
author = {Sajda, Paul and Du, Shuyan and Parra, Lucas},
doi = {10.1117/12.504676},
issn = {0277786X},
journal = {Proceedings of SPIE},
keywords = {blind source separation,chemical shift imaging,csi,hsi,hy-,nmf,nmr,non-negative matrix factorization,nuclear magnetic resonance,perspectral imaging,raman spectroscopy,spectroscopy},
pages = {321--331},
title = {{Recovery of constituent spectra using non-negative matrix factorization}},
url = {http://link.aip.org/link/?PSI/5207/321/1{\&}Agg=doi{\%}5Cnhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.868{\&}rep=rep1{\&}type=pdf},
volume = {5207},
year = {2003}
}
@article{Hakonen2015,
author = {Hakonen, Aron and Andersson, Per Ola and {Stenb{\ae}k Schmidt}, Michael and Rindzevicius, Tomas and K{\"{a}}ll, Mikael},
doi = {10.1016/j.aca.2015.04.010},
issn = {00032670},
journal = {Analytica Chimica Acta},
month = {sep},
pages = {1--13},
title = {{Explosive and chemical threat detection by surface-enhanced Raman scattering: A review}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0003267015004754},
volume = {893},
year = {2015}
}
@article{LeSu99,
abstract = {Is perception of the whole based on perception of its parts? There is psychological(1) and physiological(2,3) evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations(4,5). But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.},
address = {PORTERS SOUTH, 4 CRINAN ST, LONDON N1 9XW, ENGLAND},
author = {Lee, Daniel D. and Seung, H. Sebastian},
doi = {10.1038/44565},
file = {:home/tsal/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lee, Seung - 1999 - Learning the parts of objects by non-negative matrix factorization.pdf:pdf},
journal = {Nature},
keywords = {spie10ref19},
mendeley-tags = {spie10ref19},
month = {oct},
number = {6755},
pages = {788--791},
publisher = {MACMILLAN MAGAZINES LTD},
title = {{Learning the parts of objects by non-negative matrix factorization}},
type = {Article},
volume = {401},
year = {1999}
}
@article{Jeanmaire1977a,
author = {Jeanmaire, David L. and {Van Duyne}, Richard P.},
doi = {10.1016/S0022-0728(77)80224-6},
issn = {00220728},
journal = {Journal of Electroanalytical Chemistry and Interfacial Electrochemistry},
month = {nov},
number = {1},
pages = {1--20},
title = {{Surface raman spectroelectrochemistry}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0022072877802246},
volume = {84},
year = {1977}
}
@article{Schlucker2014,
author = {Schl{\"{u}}cker, Sebastian},
doi = {10.1002/anie.201205748},
issn = {14337851},
journal = {Angewandte Chemie International Edition},
month = {may},
number = {19},
pages = {4756--4795},
title = {{Surface-Enhanced Raman Spectroscopy: Concepts and Chemical Applications}},
url = {http://doi.wiley.com/10.1002/anie.201205748},
volume = {53},
year = {2014}
}
@article{Fleischmann1974,
author = {Fleischmann, M. and Hendra, P.J. and McQuillan, A.J.},
doi = {10.1016/0009-2614(74)85388-1},
issn = {00092614},
journal = {Chemical Physics Letters},
month = {may},
number = {2},
pages = {163--166},
title = {{Raman spectra of pyridine adsorbed at a silver electrode}},
url = {http://linkinghub.elsevier.com/retrieve/pii/0009261474853881},
volume = {26},
year = {1974}
}
@article{Wertheim1974,
abstract = {The approximation of the Voigt line shape by the sum of a Gaussian and a Lorentzian of equal width is explored. The relative intensities of these two lines in the approximate function are related to the widths of the components of the Voigt function using nonlinear least-squares fitting. The useful range of the approximation includes lines sharper than Lorentzian and flatter at the top than Gaussian.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Wertheim, G. K. and Butler, M. A. and West, K. W. and Buchanan, D. N E},
doi = {10.1063/1.1686503},
eprint = {arXiv:1011.1669v3},
isbn = {00346748 (ISSN)},
issn = {00346748},
journal = {Review of Scientific Instruments},
number = {11},
pages = {1369--1371},
pmid = {25246403},
title = {{Determination of the Gaussian and Lorentzian content of experimental line shapes}},
volume = {45},
year = {1974}
}
@article{Petryayeva2011,
author = {Petryayeva, Eleonora and Krull, Ulrich J.},
doi = {10.1016/j.aca.2011.08.020},
issn = {00032670},
journal = {Analytica Chimica Acta},
month = {nov},
number = {1},
pages = {8--24},
title = {{Localized surface plasmon resonance: Nanostructures, bioassays and biosensing—A review}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0003267011011196},
volume = {706},
year = {2011}
}
@article{FABS16,
author = {Fr{\o}hling, Kasper B. and Alstr{\o}m, Tommy S. and Bache, Michael and Schmidt, Michael S. and Schmidt, Mikkel N. and Larsen, Jan and Jakobsen, Mogens H. and Boisen, Anja},
doi = {10.1016/j.vibspec.2016.08.005},
issn = {09242031},
journal = {Vibrational Spectroscopy},
month = {sep},
pages = {331--336},
title = {{Surface-enhanced Raman spectroscopic study of DNA and 6-mercapto-1-hexanol interactions using large area mapping}},
volume = {86},
year = {2016}
}
@inproceedings{ScWH09,
abstract = {We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model order selection by estimating the marginal likelihood, and compare with the Bayesian information criterion. For computing the maximum a posteriori estimate we present an iterated conditional modes algorithm that rivals existing state-of-the-art NMF algorithms on an image feature extraction problem.},
author = {Schmidt, Mikkel N. and Winther, Ole and Hansen, Lars Kai},
booktitle = {Independent Component Analysis and Signal Separation},
doi = {10.1007/978-3-642-00599-2_68},
isbn = {978-3-642-00598-5},
issn = {0302-9743},
pages = {540--547},
series = {Lecture Notes in Computer Science},
title = {{Bayesian Non-negative Matrix Factorization}},
url = {http://link.springer.com/10.1007/978-3-642-00599-2{\_}68},
volume = {5441},
year = {2009}
}