Bibliography#

[AHK+22]

Elizabeth Armstrong, Michael A. Hansen, Robert C. Knaus, Nathaniel A. Trask, John C. Hewson, and James C. Sutherland. Accurate compression of tabulated chemistry models with partition of unity networks. Combustion Science and Technology, 0(0):1–18, 2022. doi:10.1080/00102202.2022.2102908.

[AS21]

Elizabeth Armstrong and James C. Sutherland. A technique for characterising feature size and quality of manifolds. Combustion Theory and Modelling, 25(4):646–668, 2021. doi:10.1080/13647830.2021.1931715.

[AS24]

Elizabeth Armstrong and James C. Sutherland. Reduced-order modeling with reconstruction-informed projections. Combustion and Flame, 259:113119, 2024. doi:10.1016/j.combustflame.2023.113119.

[BS15]

Amir Biglari and James C. Sutherland. An a-posteriori evaluation of principal component analysis-based models for turbulent combustion simulations. Combustion and Flame, 162(10):4025–4035, 2015. doi:10.1016/j.combustflame.2015.07.042.

[Bis06]

Christopher M Bishop. Pattern recognition and machine learning. springer, 2006.

[CVJ21]

Gunnar Carlsson and Mikael Vejdemo-Johansson. Topological Data Analysis with Applications. Cambridge University Press, 2021.

[CGP12]

Axel Coussement, Olivier Gicquel, and Alessandro Parente. Kernel density weighted principal component analysis of combustion processes. Combustion and flame, 159(9):2844–2855, 2012. doi:10.1016/j.combustflame.2012.04.004.

[DMJRM00]

Roy De Maesschalck, Delphine Jouan-Rimbaud, and Désiré L Massart. The mahalanobis distance. Chemometrics and intelligent laboratory systems, 50(1):1–18, 2000.

[EM15]

Tarek Echekki and Hessam Mirgolbabaei. Principal component transport in turbulent combustion: a posteriori analysis. Combustion and Flame, 162(5):1919–1933, 2015.

[ELL09]

Brian S. Everitt, Sabine Landau, and Morven Leese. Cluster Analysis. Wiley Publishing, 4th edition, 2009. ISBN 0340761199.

[Fro76]

Serge Frontier. Étude de la décroissance des valeurs propres dans une analyse en composantes principales: comparaison avec le modd́le du bâton brisé. Journal of experimental marine Biology and Ecology, 25(1):67–75, 1976.

[GSB04]

Abdul A. Gill, George D. Smith, and Anthony J. Bagnall. Improving decision tree performance through induction-and cluster-based stratified sampling. In International Conference on Intelligent Data Engineering and Automated Learning, 339–344. Springer, 2004.

[HAS+22]

M. A. Hansen, E. Armstrong, J. C. Sutherland, J. McConnell, J. C. Hewson, and R. Knaus. Spitfire. 2022. URL: https://spitfire.readthedocs.io.

[HS18]

Michael A. Hansen and James C. Sutherland. On the consistency of state vectors and jacobian matrices. Combustion and Flame, 193:257–271, 2018. doi:10.1016/j.combustflame.2018.03.017.

[HSSC07]

Evatt R Hawkes, Ramanan Sankaran, James C Sutherland, and Jacqueline H Chen. Scalar mixing in direct numerical simulations of temporally evolving plane jet flames with skeletal co/h2 kinetics. Proceedings of the combustion institute, 31(1):1633–1640, 2007.

[HG09]

Haibo He and Edwardo A Garcia. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9):1263–1284, 2009.

[Hardle90]

Wolfgang Härdle. Applied Nonparametric Regression. Econometric Society Monographs. Cambridge University Press, 1990. doi:10.1017/CCOL0521382483.

[Jol72]

Ian T Jolliffe. Discarding variables in a principal component analysis. i: artificial data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 21(2):160–173, 1972.

[Kai60]

Henry F. Kaiser. The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1):141–151, 1960.

[KL97]

Nandakishore Kambhatla and Todd K. Leen. Dimension reduction by local principal component analysis. Neural computation, 9(7):1493–1516, 1997.

[KR09]

Leonard Kaufman and Peter J. Rousseeuw. Finding groups in data: an introduction to cluster analysis. Volume 344. John Wiley & Sons, 2009.

[KK04]

Michael R Keenan and Paul G Kotula. Accounting for poisson noise in the multivariate analysis of tof-sims spectrum images. Surface and Interface Analysis: An International Journal devoted to the development and application of techniques for the analysis of surfaces, interfaces and thin films, 36(3):203–212, 2004.

[KEA+03]

Hector C Keun, Timothy MD Ebbels, Henrik Antti, Mary E Bollard, Olaf Beckonert, Elaine Holmes, John C Lindon, and Jeremy K Nicholson. Improved analysis of multivariate data by variable stability scaling: application to nmr-based metabolic profiling. Analytica chimica acta, 490(1-2):265–276, 2003.

[Krz87]

Wojtek J Krzanowski. Selection of variables to preserve multivariate data structure, using principal components. Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(1):22–33, 1987.

[MMD10]

Robert J. May, Holger R. Maier, and Graeme C. Dandy. Data splitting for artificial neural networks using som-based stratified sampling. Neural Networks, 23(2):283–294, 2010.

[Ney92]

Jerzy Neyman. On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. In Breakthroughs in Statistics, pages 123–150. Springer, 1992.

[Nod08]

Isao Noda. Scaling techniques to enhance two-dimensional correlation spectra. Journal of Molecular Structure, 883:216–227, 2008.

[PS13]

Alessandro Parente and James C. Sutherland. Principal component analysis of turbulent combustion data: data pre-processing and manifold sensitivity. Combustion and flame, 160(2):340–350, 2013. doi:10.1016/j.combustflame.2012.09.016.

[PSTS09]

Alessandro Parente, James C. Sutherland, Leonardo Tognotti, and Philip J. Smith. Identification of low-dimensional manifolds in turbulent flames. Proceedings of the Combustion Institute, 32(1):1579–1586, 2009. doi:10.1016/j.proci.2008.06.177.

[RLM+16]

Mojdeh Rastgoo, Guillaume Lemaitre, Joan Massich, Olivier Morel, Franck Marzani, Rafael Garcia, and Fabrice Meriaudeau. Tackling the problem of data imbalancing for melanoma classification. BIOSTEC - 3rd International Conference on BIOIMAGING, 2016.

[SCSC03]

Mei-Ling Shyu, Shu-Ching Chen, Kanoksri Sarinnapakorn, and LiWu Chang. A novel anomaly detection scheme based on principal component classifier. Technical Report, MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING, 2003.

[SP09]

James C. Sutherland and Alessandro Parente. Combustion modeling using principal component analysis. Proceedings of the Combustion Institute, 32(1):1563–1570, 2009. doi:10.1016/j.proci.2020.07.014.

[vdBHW+06]

Robert A van den Berg, Huub CJ Hoefsloot, Johan A Westerhuis, Age K Smilde, and Mariët J van der Werf. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC genomics, 7(1):1–15, 2006.

[ZdybalAPS23]

Kamila Zdybał, Elizabeth Armstrong, Alessandro Parente, and James C Sutherland. Pcafold 2.0—novel tools and algorithms for low-dimensional manifold assessment and optimization. SoftwareX, 23:101447, 2023. doi:10.1016/j.softx.2023.101447.

[ZdybalASP22]

Kamila Zdybał, Elizabeth Armstrong, James C. Sutherland, and Alessandro Parente. Cost function for low-dimensional manifold topology assessment. Scientific Reports, 12:14496, 2022. doi:10.1038/s41598-022-18655-1.

[ZdybalDAlessioA+23]

Kamila Zdybał, Giuseppe D’Alessio, Antonio Attili, Axel Coussement, James C. Sutherland, and Alessandro Parente. Local manifold learning and its link to domain-based physics knowledge. Applications in Energy and Combustion Science, 14:100131, 2023. doi:10.1016/j.jaecs.2023.100131.

[ZdybalPS23]

Kamila Zdybał, Alessandro Parente, and James C Sutherland. Improving reduced-order models through nonlinear decoding of projection-dependent outputs. Patterns, 4:100859, 2023. doi:10.1016/j.patter.2023.100859.

[ZdybalSP22]

Kamila Zdybał, James C. Sutherland, and Alessandro Parente. Manifold-informed state vector subset for reduced-order modeling. Proceedings of the Combustion Institute, 2022. doi:10.1016/j.proci.2022.06.019.

[Jolliffe02]

Ian Jolliffe. Principal component analysis. Springer Verlag, New York, 2002.