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arXiv 2203.08571math.ATcs.CG
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基于链复形的Morse理论信号压缩与重构

Morse Theoretic Signal Compression and Reconstruction on Chain Complexes

Stefania Ebli, Celia Hacker, Kelly Maggs

摘要:

在拓扑数据分析(TDA)和机器学习的交叉点上,细胞信号处理领域近年来发展迅速。在这种情况下,使用组合拉普拉斯算子和所得的霍奇分解来处理复合体的细胞上的每个信号。同时,离散的莫尔斯理论被广泛应用于通过减少复形的大小而保持其整体拓扑性质来加速计算。在本文中,我们提供了一种方法来压缩和重建的链复形,利用代数离散莫尔斯理论的工具。其主要目标是通过形变收缩核来减少和重建基链复形及其单元上的一组信号,尽可能多地保留复合体和信号的全局拓扑结构。首先证明了有限维实度链复形的任何形变收缩核都等价于Morse匹配。然后,我们将研究在特定类型的Morse匹配下信号如何变化,表明其重构误差在Hodge分解的特定分量上是微不足道的。此外,我们提供了一个算法来计算Morse匹配与最小的重建误差。

英文摘要:

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal on the cells of a complex is processed using the combinatorial Laplacian, and the resultant Hodge decomposition. Meanwhile, discrete Morse theory has been widely used to speed up computations by reducing the size of complexes while preserving their global topological properties. In this paper, we provide an approach to signal compression and reconstruction on chain complexes that leverages the tools of algebraic discrete Morse theory. The main goal is to reduce and reconstruct a based chain complex together with a set of signals on its cells via deformation retracts, preserving as much as possible the global topological structure of both the complex and the signals. We first prove that any deformation retract of real degree-wise finite-dimensional based chain complexes is equivalent to a Morse matching. We will then study how the signal changes under particular types of Morse matching, showing its reconstruction error is trivial on specific components of the Hodge decomposition. Furthermore, we provide an algorithm to compute Morse matchings with minimal reconstruction error.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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