The combination of persistent homology and discrete Morse theory has proven very effective in visualizing and analyzing big and heterogeneous data. Indeed, topology provides computable and coarse summaries of data independently from specific coordinate systems and does so robustly to noise. Moreover, the geometric content of a discrete gradient vector field is very useful for visualization purposes. The specific case of multivariate data still demands for further investigations, on the one hand, for computational reasons, it is important to reduce the necessary amount of data to be processed. On the other hand, for analysis reasons, the multivariate case requires the detection and interpretation of the possible interdepedance among data components. To this end, in this paper we introduce and study a notion of perfectness for discrete gradient vector fields with respect to multi-parameter persistent homology, called relative-perfectness. As a natural generalization of usual perfectness in Morse theory for homology, relative-perfectness entails having the least number of critical cells relevant for multi-parameter persistence. As a first contribution, we support our definition of relative-perfectness by generalizing Morse inequalities to the filtration structure where homology groups involved are relative with respect to subsequent sublevel sets. In order to allow for an interpretation of critical cells in $2$-parameter persistence, our second contribution consists of two inequalities bounding Betti tables of persistence modules from above and below, via the number of critical cells. Our last result is the proof that existing algorithms based on local homotopy expansions allow for efficient computability over simplicial complexes up to dimension $2$.
翻译:持久性同质和离散摩尔斯理论的结合证明在可视化和分析大型和多元数据方面非常有效。 事实上, 地形学提供了独立于特定协调系统、对噪音的可比较和粗粗的数据摘要。 此外, 离散梯度矢量场的几何内容对于可视化目的非常有用。 多变量数据的具体案例仍然需要进一步调查, 一方面, 出于计算原因, 多变量数据需要减少需要处理的数据数量。 另一方面, 由于分析原因, 多变量案例需要检测和解释数据组成部分之间可能的互换性。 为此, 我们在本文件中提出并研究离散梯度矢量场的完美性概念, 相对于多参数持久性, 称为相对性。 多变量理论通常的完美性, 相对性意味着要减少需要处理的数据数量。 由于分析的原因, 多方位扩张性案例, 多变量案例需要检测和解释。 作为第一项贡献, 我们支持我们定义的相对性的相对性定义, 通过常规化的美元分解运量的分解度字段, 也就是我们相对性分解的分解的分解层结构。