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Dimensionality reduction is a crucial technique in data analysis, as it allows for the efficient visualization and understanding of high-dimensional datasets.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
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Analysis of a complex of statistical variables into principal components
Harold Hotelling · 1933
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Lsqr: An algorithm for sparse linear equations and sparse least squares
Christopher C Paige and Michael A Saunders · 1982
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Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1994
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Columbia object image library (coil-20)
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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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Visualizing data using t-sne
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Towards a theoretical foundation for Laplacian-based manifold methods
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Persistent cohomology and circular coordinates
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Statistical ranking and combinatorial hodge theory
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Scikit-learn: Machine learning in Python
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Hodge Laplacians on graphs
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Generalized penalty for circular coordinate representation
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Ripser: efficient computation of vietoris–rips persistence barcodes
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