Nonnegative decomposition of functional count data
We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a high...
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| Vydáno v: | Biometrics Ročník 76; číslo 4; s. 1273 - 1284 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Blackwell Publishing Ltd
01.12.2020
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| ISSN: | 0006-341X, 1541-0420, 1541-0420 |
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| Abstract | We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans. |
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| AbstractList | We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans. We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans. We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans.We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans. |
| Author | Backenroth, Daniel Schrack, Jennifer A. Shinohara, Russell T. Goldsmith, Jeff |
| Author_xml | – sequence: 1 givenname: Daniel orcidid: 0000-0002-9581-8870 surname: Backenroth fullname: Backenroth, Daniel email: daniel.backenroth@gmail.com organization: Columbia University – sequence: 2 givenname: Russell T. orcidid: 0000-0001-8627-8203 surname: Shinohara fullname: Shinohara, Russell T. organization: University of Pennsylvania – sequence: 3 givenname: Jennifer A. surname: Schrack fullname: Schrack, Jennifer A. organization: Johns Hopkins Bloomberg School of Public Health – sequence: 4 givenname: Jeff surname: Goldsmith fullname: Goldsmith, Jeff organization: Columbia University |
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| Cites_doi | 10.1111/j.2517-6161.1964.tb00553.x 10.1214/009053604000001156 10.1093/biostatistics/kxt029 10.1111/j.1541-0420.2012.01808.x 10.1111/biom.12083 10.1093/biomet/asn010 10.1145/1102351.1102451 10.1093/gerona/glt199 10.1007/978-0-387-21706-2 10.1198/106186002853 10.1198/016214504000001745 10.1007/s10182-009-0113-6 10.1137/0916069 10.1214/08-AOAS206 10.1146/annurev-statistics-041715-033624 10.1016/j.neuroimage.2014.11.045 10.1111/biom.12963 10.1214/18-AOAS1135 10.1109/TKDE.2012.51 10.1111/j.1467-9868.2010.00749.x 10.1007/s00421-005-0112-6 10.1109/ASPAA.2003.1285860 10.1111/j.1467-9868.2008.00656.x 10.1016/j.csda.2016.07.010 10.1111/biom.12278 10.1038/44565 10.1561/2200000055 10.1109/TSP.2013.2285514 |
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| References | 2006; 96 2013; 69 2013; 25 2012 2015; 71 2019; 75 1995; 16 2013; 62 2002; 11 2014; 69 2003; 16 2014; 24 2005 2015; 108 1964; 26 2003 2002 2012; 13 2008; 95 2008; 70 1999; 401 2016; 3 2001 2005; 100 2009; 93 2011; 73 2014; 15 2017 2016 2009; 4 2018; 12 2005; 33 2017; 105 2016; 9 e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 Donoho D.L. (e_1_2_8_10_1) 2003; 16 e_1_2_8_26_1 e_1_2_8_27_1 Du P. (e_1_2_8_11_1) 2014; 24 e_1_2_8_3_1 Ypma J. (e_1_2_8_37_1) 2017 e_1_2_8_2_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 Gillis N. (e_1_2_8_13_1) 2012; 13 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_16_1 Lin X. (e_1_2_8_20_1) 2016 Box G.E.P. (e_1_2_8_5_1) 1964; 26 e_1_2_8_32_1 e_1_2_8_31_1 e_1_2_8_34_1 Bertsekas D.P. (e_1_2_8_4_1) 2012 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 |
| References_xml | – volume: 69 start-page: 973 year: 2014 end-page: 979 article-title: Assessing the “physical cliff”: detailed quantification of aging and physical activity publication-title: Journal of Gerontology: Medical Sciences – volume: 33 start-page: 774 year: 2005 end-page: 805 article-title: Generalized functional linear models publication-title: Annals of Statistics – volume: 401 start-page: 788 year: 1999 end-page: 791 article-title: Learning the parts of objects by non‐negative matrix factorization publication-title: Nature – start-page: 85 year: 2012 end-page: 120 – volume: 73 start-page: 3 year: 2011 end-page: 36 article-title: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models publication-title: Journal of the Royal Statistical Society: Series B – start-page: 1 year: 2005 end-page: 4 article-title: First results on uniqueness of sparse non‐negative matrix factorization – start-page: 617 year: 2001 end-page: 624 – start-page: 792 year: 2005 end-page: 799 article-title: Non‐negative tensor factorization with applications to statistics and computer vision – volume: 96 start-page: 517 year: 2006 end-page: 524 article-title: Effect of combined movement and heart rate monitor placement on physical activity estimates during treadmill locomotion and free‐living publication-title: European Journal of Applied Physiology – volume: 16 start-page: 1190 year: 1995 end-page: 1208 article-title: A limited memory algorithm for bound constrained optimization publication-title: SIAM Journal on Scientific Computing – volume: 24 start-page: 1017 year: 2014 end-page: 1041 article-title: Penalized likelihood functional regression publication-title: Statistica Sinica – volume: 69 start-page: 903 year: 2013 end-page: 913 article-title: Multilevel cross‐dependent binary longitudinal data publication-title: Biometrics – volume: 75 start-page: 48 year: 2019 end-page: 57 article-title: Registration for exponential family functional data publication-title: Biometrics – year: 2016 – volume: 13 start-page: 3349 year: 2012 end-page: 3386 article-title: Sparse and unique nonnegative matrix factorization through data preprocessing publication-title: Journal of Machine Learning Research – volume: 95 start-page: 415 year: 2008 end-page: 436 article-title: On the asymptotics of penalized splines publication-title: Biometrika – volume: 11 start-page: 735 year: 2002 end-page: 757 article-title: Selecting the number of knots for penalized splines publication-title: Journal of Computational and Graphical Statistics – volume: 108 start-page: 1 year: 2015 end-page: 16 article-title: Finding imaging patterns of structural covariance via non‐negative matrix factorization publication-title: NeuroImage – volume: 105 start-page: 46 year: 2017 end-page: 52 article-title: A note on modeling sparse exponential‐family functional response curves publication-title: Computational Statistics and Data Analysis – volume: 93 start-page: 307 year: 2009 end-page: 333 article-title: A Bayesian latent variable approach to functional principal components analysis with binary and count publication-title: Advances in Statistical Analysis – volume: 100 start-page: 577 year: 2005 end-page: 590 article-title: Functional data analysis for sparse longitudinal data publication-title: Journal of the American Statistical Association – volume: 69 start-page: 41 year: 2013 end-page: 51 article-title: Corrected confidence bands for functional data using principal components publication-title: Biometrics – start-page: 177 year: 2003 end-page: 180 article-title: Non‐negative matrix factorization for polyphonic music transcription – volume: 70 start-page: 703 year: 2008 end-page: 723 article-title: Modelling sparse generalized longitudinal observations with latent Gaussian processes publication-title: Journal of the Royal Statistical Society: Series B – volume: 71 start-page: 344 year: 2015 end-page: 353 article-title: Generalized multilevel function‐on‐scalar regression and principal component analysis publication-title: Biometrics – volume: 62 start-page: 211 year: 2013 end-page: 224 article-title: Non‐negative matrix factorization revisited: uniqueness and algorithm for symmetric decomposition publication-title: IEEE Transactions on Signal Processing – year: 2002 – volume: 3 start-page: 257 year: 2016 end-page: 295 article-title: Functional data analysis publication-title: Annual Review of Statistics and Its Application – volume: 26 start-page: 211 year: 1964 end-page: 252 article-title: An analysis of transformations (with discussion) publication-title: Journal of the Royal Statistical Society B – volume: 9 start-page: 1 year: 2016 end-page: 118 article-title: Generalized low rank models publication-title: Foundations and Trends® in Machine Learning – volume: 12 start-page: 1871 year: 2018 end-page: 1893 article-title: Functional principal variance component testing for a genetic association study of HIV progression publication-title: The Annals of Applied Statistics – year: 2017 – volume: 25 start-page: 1336 year: 2013 end-page: 1353 article-title: Nonnegative matrix factorization: a comprehensive review publication-title: IEEE Transactions on Knowledge and Data Engineering – volume: 15 start-page: 102 year: 2014 end-page: 116 article-title: Normalization and extraction of interpretable metrics from raw accelerometry data publication-title: Biostatistics – volume: 4 start-page: 458 year: 2009 end-page: 488 article-title: Multilevel functional principal component analysis publication-title: Annals of Applied Statistics – volume: 16 start-page: 1141 year: 2003 end-page: 1148 article-title: When does non‐negative matrix factorization give a correct decomposition into parts? publication-title: Advances in Neural Information Processing Systems – volume: 13 start-page: 3349 year: 2012 ident: e_1_2_8_13_1 article-title: Sparse and unique nonnegative matrix factorization through data preprocessing publication-title: Journal of Machine Learning Research – start-page: 85 volume-title: Optimization for Machine Learning year: 2012 ident: e_1_2_8_4_1 – volume: 26 start-page: 211 year: 1964 ident: e_1_2_8_5_1 article-title: An analysis of transformations (with discussion) publication-title: Journal of the Royal Statistical Society B doi: 10.1111/j.2517-6161.1964.tb00553.x – volume-title: NNLM: Fast and Versatile Non‐Negative Matrix Factorization year: 2016 ident: e_1_2_8_20_1 – ident: e_1_2_8_21_1 doi: 10.1214/009053604000001156 – ident: e_1_2_8_3_1 doi: 10.1093/biostatistics/kxt029 – ident: e_1_2_8_14_1 doi: 10.1111/j.1541-0420.2012.01808.x – ident: e_1_2_8_24_1 doi: 10.1111/biom.12083 – ident: e_1_2_8_19_1 doi: 10.1093/biomet/asn010 – ident: e_1_2_8_25_1 doi: 10.1145/1102351.1102451 – ident: e_1_2_8_23_1 doi: 10.1093/gerona/glt199 – ident: e_1_2_8_31_1 doi: 10.1007/978-0-387-21706-2 – volume: 16 start-page: 1141 year: 2003 ident: e_1_2_8_10_1 article-title: When does non‐negative matrix factorization give a correct decomposition into parts? publication-title: Advances in Neural Information Processing Systems – ident: e_1_2_8_22_1 doi: 10.1198/106186002853 – ident: e_1_2_8_28_1 – ident: e_1_2_8_36_1 doi: 10.1198/016214504000001745 – ident: e_1_2_8_30_1 doi: 10.1007/s10182-009-0113-6 – ident: e_1_2_8_7_1 doi: 10.1137/0916069 – ident: e_1_2_8_9_1 doi: 10.1214/08-AOAS206 – volume-title: nloptr: R Interface to NLopt year: 2017 ident: e_1_2_8_37_1 – ident: e_1_2_8_32_1 doi: 10.1146/annurev-statistics-041715-033624 – volume: 24 start-page: 1017 year: 2014 ident: e_1_2_8_11_1 article-title: Penalized likelihood functional regression publication-title: Statistica Sinica – ident: e_1_2_8_27_1 doi: 10.1016/j.neuroimage.2014.11.045 – ident: e_1_2_8_35_1 doi: 10.1111/biom.12963 – ident: e_1_2_8_2_1 doi: 10.1214/18-AOAS1135 – ident: e_1_2_8_33_1 doi: 10.1109/TKDE.2012.51 – ident: e_1_2_8_34_1 doi: 10.1111/j.1467-9868.2010.00749.x – ident: e_1_2_8_8_1 – ident: e_1_2_8_6_1 doi: 10.1007/s00421-005-0112-6 – ident: e_1_2_8_26_1 doi: 10.1109/ASPAA.2003.1285860 – ident: e_1_2_8_16_1 doi: 10.1111/j.1467-9868.2008.00656.x – ident: e_1_2_8_12_1 doi: 10.1016/j.csda.2016.07.010 – ident: e_1_2_8_15_1 doi: 10.1111/biom.12278 – ident: e_1_2_8_18_1 doi: 10.1038/44565 – ident: e_1_2_8_29_1 doi: 10.1561/2200000055 – ident: e_1_2_8_17_1 doi: 10.1109/TSP.2013.2285514 |
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| SubjectTerms | Accelerometers Algorithms data collection Decomposition functional data Functionals Multiplication nonnegative matrix factorization Physical activity principal component analysis Principal components analysis |
| Title | Nonnegative decomposition of functional count data |
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