Finding the homology of manifolds using ellipsoids

Sara Kališnik, Davorin Lešnik
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引用次数: 2

Abstract

Abstract A standard problem in applied topology is how to discover topological invariants of data from a noisy point cloud that approximates it. We consider the case where a sample is drawn from a properly embedded "Equation missing"-submanifold without boundary in a Euclidean space. We show that we can deformation retract the union of ellipsoids, centered at sample points and stretching in the tangent directions, to the manifold. Hence the homotopy type, and therefore also the homology type, of the manifold is the same as that of the nerve complex of the cover by ellipsoids. By thickening sample points to ellipsoids rather than balls, our results require a smaller sample density than comparable results in the literature. They also advocate using elongated shapes in the construction of barcodes in persistent homology.
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利用椭球寻找流形的同调性
摘要应用拓扑学中的一个标准问题是如何从与之近似的噪声点云中发现数据的拓扑不变量。我们考虑从欧几里得空间中正确嵌入的“缺失方程”-无边界子流形中抽取样本的情况。我们证明了我们可以将以采样点为中心并在切线方向上拉伸的椭球体的结合变形回缩到流形上。因此流形的同伦类型,也就是同伦类型,与椭球盖的神经复合体的同伦类型是相同的。通过将样本点加厚为椭球而不是球,我们的结果需要比文献中可比结果更小的样本密度。他们还提倡在构建持久同源的条形码时使用细长的形状。
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Proceedings of ATMCS10 Homotopy, homology, and persistent homology using closure spaces Ambiguities in neural-network-based hyperedge prediction Depth in arrangements: Dehn–Sommerville–Euler relations with applications Torsion-weighted spanning acycle entropy in cubical lattices and Mahler measures
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