Approximate and discrete Euclidean vector bundles

We introduce ε-approximate versions of the notion of Euclidean vector bundle for ε≥ 0, which recover the classical notion of Euclidean vector bundle when ε= 0. In particular, we study Čech cochains with coefficients in the orthogonal group that satisfy an approximate cocycle condition. We show that ε-approximate vector bundles can be used to represent classical vector bundles when ε > 0 is sufficiently small. We also introduce distances between approximate vector bundles and use them to prove that sufficiently similar approximate vector bundles represent the same classical vector bundle. This gives a way of specifying vector bundles over finite simplicial complexes using a finite amount of data, and also allows for some tolerance to noise when working with vector bundles in an applied setting. As an example, we prove a reconstruction theorem for vector bundles from finite samples. We give algorithms for the effective computation of low-dimensional characteristic classes of vector bundles directly from discrete and approximate representations and illustrate the usage of these algorithms with computational examples.

[1]  Mehmet Emre Çek,et al.  Analysis of observed chaotic data , 2004 .

[2]  H. O. Erdin Characteristic Classes , 2004 .

[3]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[4]  K. Brown,et al.  Graduate Texts in Mathematics , 1982 .

[5]  On Some Recent Interactions Between Mathematics and Physics , 1985, Canadian Mathematical Bulletin.

[6]  F. W. Warner Foundations of Differentiable Manifolds and Lie Groups , 1971 .

[7]  M. Heel,et al.  Single-particle electron cryo-microscopy: towards atomic resolution , 2000, Quarterly Reviews of Biophysics.

[8]  Vector bundles and Gromov-Hausdorff distance , 2006, math/0608266.

[9]  Jean-Daniel Boissonnat,et al.  The reach, metric distortion, geodesic convexity and the variation of tangent spaces , 2019, Journal of Applied and Computational Topology.

[10]  N. Steenrod Topology of Fibre Bundles , 1951 .

[11]  Robert Ghrist,et al.  Toward a spectral theory of cellular sheaves , 2018, Journal of Applied and Computational Topology.

[12]  M. Maggioni,et al.  Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[13]  R. C. Kirbyl,et al.  Pin Structures on Low-dimensional Manifolds by , 2008 .

[14]  Vin de Silva,et al.  On the Local Behavior of Spaces of Natural Images , 2007, International Journal of Computer Vision.

[15]  R. Ho Algebraic Topology , 2022 .

[16]  Felix Knöppel,et al.  Complex Line Bundles over Simplicial Complexes and their Applications , 2015, ArXiv.

[17]  Arcwise Isometries,et al.  A Course in Metric Geometry , 2001 .

[18]  R. Switzer Vector Bundles and K-Theory , 2002 .

[19]  Bill Casselman,et al.  Clifford Algebras and Spinors , 1997 .

[20]  A. Singer,et al.  Representation theoretic patterns in three dimensional Cryo-Electron Microscopy I: The intrinsic reconstitution algorithm. , 2009, Annals of mathematics.

[21]  M. Gromov Metric Structures for Riemannian and Non-Riemannian Spaces , 1999 .

[22]  Themistocles M. Rassias,et al.  Introduction to Riemannian Manifolds , 2001 .

[23]  Lek-Heng Lim,et al.  Cohomology of Cryo-Electron Microscopy , 2016, SIAM J. Appl. Algebra Geom..

[24]  André Lieutier,et al.  Vietoris-rips complexes also provide topologically correct reconstructions of sampled shapes , 2011, SoCG '11.

[25]  Endre Süli,et al.  Foundations of Computational Mathematics, Santander 2005 (London Mathematical Society Lecture Note Series) , 2006 .

[26]  Geoffrey E. Hinton,et al.  Transforming Auto-Encoders , 2011, ICANN.

[27]  R. Ghrist Barcodes: The persistent topology of data , 2007 .

[28]  A. Singer,et al.  Orientability and Diffusion Maps. , 2011, Applied and computational harmonic analysis.

[29]  Osman Berat Okutan,et al.  Vietoris-Rips Persistent Homology, Injective Metric Spaces, and The Filling Radius , 2020, ArXiv.

[30]  J. Graver,et al.  Graduate studies in mathematics , 1993 .

[31]  W. Marzantowicz,et al.  How many simplices are needed to triangulate a Grassmannian? , 2020, 2001.08292.

[32]  J. Frank Three-Dimensional Electron Microscopy of Macromolecular Assemblies: Visualization of Biological Molecules in Their Native State , 1996 .

[33]  D. Mclaughlin Local formulae for Stiefel-Whitney classes , 1996 .

[34]  J. Marsden,et al.  Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows , 2005 .

[35]  Loring W. Tu,et al.  Differential forms in algebraic topology , 1982, Graduate texts in mathematics.

[36]  Michael Robinson,et al.  Sheaves are the canonical data structure for sensor integration , 2017, Inf. Fusion.

[37]  Jose A. Perea,et al.  A Klein-Bottle-Based Dictionary for Texture Representation , 2014, International Journal of Computer Vision.

[38]  Stephen Smale,et al.  Finding the Homology of Submanifolds with High Confidence from Random Samples , 2008, Discret. Comput. Geom..

[39]  Sayan Mukherjee,et al.  The Geometry of Synchronization Problems and Learning Group Actions , 2016, Discrete & Computational Geometry.

[40]  Lek-Heng Lim,et al.  Topology of deep neural networks , 2020, J. Mach. Learn. Res..

[41]  A. S. Mishchenko,et al.  Vector Bundles and Their Applications , 2010 .

[42]  J. Jost Riemannian geometry and geometric analysis , 1995 .

[43]  K. Maurin Representations of Compact Lie Groups , 1997 .

[44]  Martin Schottenloher,et al.  Basic Bundle Theory and K-Cohomology Invariants , 2007 .

[45]  Amit Singer,et al.  Representation Theoretic Patterns in Three-Dimensional Cryo-Electron Microscopy II—The Class Averaging Problem , 2011, Found. Comput. Math..

[46]  Zhizhen Zhao,et al.  Viewing Angle Classification of Cryo-Electron Microscopy Images Using Eigenvectors , 2011, SIAM J. Imaging Sci..

[47]  Michael Robinson,et al.  Assignments to sheaves of pseudometric spaces , 2018, Compositionality.

[48]  Guillermo Artana,et al.  Topology of dynamical reconstructions from Lagrangian data , 2020 .

[49]  Rickard Brüel Gabrielsson,et al.  Exposition and Interpretation of the Topology of Neural Networks , 2018, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

[50]  Ulrich Bauer,et al.  Ripser: efficient computation of Vietoris–Rips persistence barcodes , 2019, Journal of Applied and Computational Topology.

[51]  R. F. Williams,et al.  Expanding attractors , 1974 .

[52]  Christopher J. Tralie,et al.  Ripser.py: A Lean Persistent Homology Library for Python , 2018, J. Open Source Softw..

[53]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[54]  Michael Lin,et al.  Twisty Takens: a geometric characterization of good observations on dense trajectories , 2018, Journal of Applied and Computational Topology.

[55]  F. Takens Detecting strange attractors in turbulence , 1981 .

[56]  Raphael Tinarrage Computing persistent Stiefel-Whitney classes of line bundles , 2021, Journal of Applied and Computational Topology.

[57]  J. Brylinski,et al.  Čech cocycles for characteristic classes , 1996 .