13 results on '"Lim, Sunhyuk"'
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2. The Gromov-Wasserstein distance between spheres
- Author
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Arya, Shreya, Auddy, Arnab, Edmonds, Ranthony, Lim, Sunhyuk, Memoli, Facundo, and Packer, Daniel
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Mathematics - Metric Geometry ,Mathematics - Optimization and Control - Abstract
In this paper we consider a two-parameter family {dGWp,q}p,q of Gromov- Wasserstein distances between metric measure spaces. By exploiting a suitable interaction between specific values of the parameters p and q and the metric of the underlying spaces, we determine the exact value of the distance dGW4,2 between all pairs of unit spheres of different dimension endowed with their Euclidean distance and their uniform measure., Comment: 1. Added a Section 4, Section 5, and Appendix C; 2. Swapped Sections 2 and 3; 3. "Relagated Proofs" section (Section 4 in the old version) is now in appendix
- Published
- 2023
3. Reverse Bernstein Inequality on the Circle
- Author
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Joharinad, Parvaneh, Jost, Jürgen, Lim, Sunhyuk, and Matveev, Rostislav
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Mathematics - Classical Analysis and ODEs ,42A05 - Abstract
The more then hundred years old Bernstein inequality states that the supremum norm of the derivative of a trigonometric polynomial of fixed degree can be bounded from above by supremum norm of the polynomial itself. The reversed Bernstein inequality, that we prove in this note, says that the reverse inequality holds for functions in the orthogonal complement of the space of polynomials of fixed degree. In fact, we derived a more general result for the lower bounds on higher derivatives. These bounds are better then those obtained by applying bound for the first derivative successively several times., Comment: 8 pages, additional references in v2
- Published
- 2023
4. The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs
- Author
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Chen, Samantha, Lim, Sunhyuk, Mémoli, Facundo, Wan, Zhengchao, and Wang, Yusu
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Computer Science - Machine Learning - Abstract
In this paper, we present a novel interpretation of the so-called Weisfeiler-Lehman (WL) distance, introduced by Chen et al. (2022), using concepts from stochastic processes. The WL distance aims at comparing graphs with node features, has the same discriminative power as the classic Weisfeiler-Lehman graph isomorphism test and has deep connections to the Gromov-Wasserstein distance. This new interpretation connects the WL distance to the literature on distances for stochastic processes, which also makes the interpretation of the distance more accessible and intuitive. We further explore the connections between the WL distance and certain Message Passing Neural Networks, and discuss the implications of the WL distance for understanding the Lipschitz property and the universal approximation results for these networks.
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- 2023
5. Gromov-Hausdorff distances, Borsuk-Ulam theorems, and Vietoris-Rips complexes
- Author
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Adams, Henry, Bush, Johnathan, Clause, Nate, Frick, Florian, Gómez, Mario, Harrison, Michael, Jeffs, R. Amzi, Lagoda, Evgeniya, Lim, Sunhyuk, Mémoli, Facundo, Moy, Michael, Sadovek, Nikola, Superdock, Matt, Vargas, Daniel, Wang, Qingsong, and Zhou, Ling
- Subjects
Mathematics - Metric Geometry ,Mathematics - Algebraic Topology ,Mathematics - Geometric Topology ,51F30, 53C23, 55N31, 55P91 - Abstract
We explore emerging relationships between the Gromov-Hausdorff distance, Borsuk-Ulam theorems, and Vietoris-Rips simplicial complexes. The Gromov-Hausdorff distance between two metric spaces $X$ and $Y$ can be lower bounded by the distortion of (possibly discontinuous) functions between them. The more these functions must distort the metrics, the larger the Gromov-Hausdorff distance must be. Topology has few tools to obstruct the existence of discontinuous functions. However, an arbitrary function $f\colon X\to Y$ induces a continuous map between their Vietoris-Rips simplicial complexes, where the allowable choices of scale parameters depend on how much the function $f$ distorts distances. We can then use equivariant topology to obstruct the existence of certain continuous maps between Vietoris-Rips complexes. With these ideas we bound how discontinuous an odd map between spheres $S^k\to S^n$ with $k>n$ must be, generalizing a result by Dubins and Schwarz (1981), which is the case $k=n+1$. As an application, we recover or improve upon all of the lower bounds from Lim, M\'emoli, and Smith (2022) on the Gromov-Hausdorff distances between spheres of different dimensions. We also provide new upper bounds on the Gromov-Hausdorff distance between spheres of adjacent dimensions.
- Published
- 2022
6. Weisfeiler-Lehman meets Gromov-Wasserstein
- Author
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Chen, Samantha, Lim, Sunhyuk, Mémoli, Facundo, Wan, Zhengchao, and Wang, Yusu
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Computer Science - Machine Learning ,Mathematics - Metric Geometry - Abstract
The Weisfeiler-Lehman (WL) test is a classical procedure for graph isomorphism testing. The WL test has also been widely used both for designing graph kernels and for analyzing graph neural networks. In this paper, we propose the Weisfeiler-Lehman (WL) distance, a notion of distance between labeled measure Markov chains (LMMCs), of which labeled graphs are special cases. The WL distance is polynomial time computable and is also compatible with the WL test in the sense that the former is positive if and only if the WL test can distinguish the two involved graphs. The WL distance captures and compares subtle structures of the underlying LMMCs and, as a consequence of this, it is more discriminating than the distance between graphs used for defining the state-of-the-art Wasserstein Weisfeiler-Lehman graph kernel. Inspired by the structure of the WL distance we identify a neural network architecture on LMMCs which turns out to be universal w.r.t. continuous functions defined on the space of all LMMCs (which includes all graphs) endowed with the WL distance. Finally, the WL distance turns out to be stable w.r.t. a natural variant of the Gromov-Wasserstein (GW) distance for comparing metric Markov chains that we identify. Hence, the WL distance can also be construed as a polynomial time lower bound for the GW distance which is in general NP-hard to compute.
- Published
- 2022
7. Classical Multidimensional Scaling on Metric Measure Spaces
- Author
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Lim, Sunhyuk and Memoli, Facundo
- Subjects
Mathematics - Functional Analysis ,Mathematics - Metric Geometry - Abstract
We generalize the classical Multidimensional Scaling procedure to the setting of general metric measure spaces. We develop a related spectral theory for the generalized cMDS operator, which provides a more natural and rigorous mathematical background for cMDS. Also, we show that the sum of all negative eigenvalues of the cMDS operator is a new invariant measuring non-flatness of a metric measure space. Furthermore, the cMDS output of several non-finite exemplar metric measures spaces, in particular the cMDS for spheres S^{d-1} and subsets of Euclidean space, are studied. Finally, we prove the stability of the generalized cMDS process with respect to the Gromov-Wasserstein distance., Comment: Major changes are the following: (1) Fixed the proof of Proposition 3.25 (2) We wrote a new Section 7 for further discussion
- Published
- 2022
8. Some results about the Tight Span of spheres
- Author
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Lim, Sunhyuk, Memoli, Facundo, Wan, Zhengchao, Wang, Qingsong, and Zhou, Ling
- Subjects
Mathematics - Metric Geometry ,Mathematics - Algebraic Topology - Abstract
The smallest hyperconvex metric space containing a given metric space X is called the tight span of X. It is known that tight spans have many nice geometric and topological properties, and they are gradually becoming a target of research of both the metric geometry community and the topological/geometric data analysis community. In this paper, we study the tight span of n-spheres (with either geodesic metric or l infinity-metric).
- Published
- 2021
9. The Gromov-Hausdorff distance between spheres
- Author
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Lim, Sunhyuk, Mémoli, Facundo, and Smith, Zane
- Subjects
Mathematics - Metric Geometry ,Mathematics - Algebraic Topology ,Mathematics - Differential Geometry - Abstract
We provide general upper and lower bounds for the Gromov-Hausdorff distance $d_{\mathrm{GH}}(\mathbb{S}^m,\mathbb{S}^n)$ between spheres $\mathbb{S}^m$ and $\mathbb{S}^n$ (endowed with the round metric) for $0\leq m< n\leq \infty$. Some of these lower bounds are based on certain topological ideas related to the Borsuk-Ulam theorem. Via explicit constructions of (optimal) correspondences we prove that our lower bounds are tight in the cases of $d_{\mathrm{GH}}(\mathbb{S}^0,\mathbb{S}^n)$, $d_{\mathrm{GH}}(\mathbb{S}^m,\mathbb{S}^\infty)$, $d_{\mathrm{GH}}(\mathbb{S}^1,\mathbb{S}^2)$, $d_{\mathrm{GH}}(\mathbb{S}^1,\mathbb{S}^3)$ and $d_{\mathrm{GH}}(\mathbb{S}^2,\mathbb{S}^3)$. We also formulate a number of open questions., Comment: * We made some structural changes for better readability
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- 2021
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10. Vietoris-Rips Persistent Homology, Injective Metric Spaces, and The Filling Radius
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Lim, Sunhyuk, Memoli, Facundo, and Okutan, Osman Berat
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Mathematics - Algebraic Topology ,Computer Science - Computational Geometry - Abstract
In the applied algebraic topology community, the persistent homology induced by the Vietoris-Rips simplicial filtration is a standard method for capturing topological information from metric spaces. In this paper, we consider a different, more geometric way of generating persistent homology of metric spaces which arises by first embedding a given metric space into a larger space and then considering thickenings of the original space inside this ambient metric space. In the course of doing this, we construct an appropriate category for studying this notion of persistent homology and show that, in a category theoretic sense, the standard persistent homology of the Vietoris-Rips filtration is isomorphic to our geometric persistent homology provided that the ambient metric space satisfies a property called injectivity. As an application of this isomorphism result we are able to precisely characterize the type of intervals that appear in the persistence barcodes of the Vietoris-Rips filtration of any compact metric space and also to give succinct proofs of the characterization of the persistent homology of products and metric gluings of metric spaces. Our results also permit proving several bounds on the length of intervals in the Vietoris-Rips barcode by other metric invariants. Finally, as another application, we connect this geometric persistent homology to the notion of filling radius of manifolds introduced by Gromov \cite{G07} and show some consequences related to (1) the homotopy type of the Vietoris-Rips complexes of spheres which follow from work of M.~Katz and (2) characterization (rigidity) results for spheres in terms of their Vietoris-Rips persistence barcodes which follow from work of F.~Wilhelm., Comment: We added Remark 9.8
- Published
- 2020
- Full Text
- View/download PDF
11. Classical multidimensional scaling on metric measure spaces
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Lim, Sunhyuk, primary and Mémoli, Facundo, additional
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- 2024
- Full Text
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12. The Gromov–Hausdorff distance between spheres
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Lim, Sunhyuk, primary, Mémoli, Facundo, additional, and Smith, Zane, additional
- Published
- 2023
- Full Text
- View/download PDF
13. Geometry, Topology, and Spectral Methods in Data Analysis: from Injective Metric Spaces, through Gromov-type Distances, to Generalized MDS
- Author
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Lim, Sunhyuk
- Subjects
- Mathematics
- Abstract
In this dissertation, I will explain four ideas on the Geometry and Topology of metric (measure) spaces, and its connection to Data Analysis. The first project is "Vietoris-Rips Persistent Homology, Injective Metric Spaces, and The Filling Radius". In this project, We build a precise connection between the Vietoris-Rips filtration and the metric thickening filtration in injective metric spaces. As a result, we are able to achieve deeper understanding of the relationship between persistence barcodes and the geometry of the underlying dataset. The second project is "The Gromov-Hausdorff distance between spheres". In this project, we compute some nontrivial (and in several cases, tight) estimates of the Gromov-Hausdorff distances between spheres via Borsuk-Ulam type arguments, and via the construction of explicit (optimal) correspondences between spheres of different dimension. The third project is "Gromov-Markov distances between Markov chains". In this project, we build spectral distances between finite spaces by using Markov chains as a proxy for heat kernels. Finally, the last project is "Generalization of cMDS to metric measure spaces". In this project, we generalized the classical Multidimensional scaling for finite metric spaces to the metric measure space setting. As a result, we have rigorous and natural mathematical foundation for cMDS, which gives us new way to quantify non-flatness of metric measure spaces.
- Published
- 2021
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