The main objective of this paper is to introduce and study a notion of perfectness for discrete gradient vector fields with respect to ( multi-parameter ) persistent homology . As a natural generalization of usual perfectness in Morse theory for homology, persistence-perfectness entails having the least number of critical cells relevant for persistent homology. The first result about a persistence-perfect gradient vector-field is that the number of its critical cells yield inequalities bounding the Betti tables of persistence modules , as a sort of Morse inequalities for multi-parameter persistence. The second result is that, at least in low dimensions, persistence-perfect gradient vector-fields not only exist but can be constructed by an algorithm based on local homotopy expansions . These results show a link between multi-parameter persistence and discrete Morse theory that can be leveraged for a better understanding of the former.
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.
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main objective of this paper is to
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
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persistent homology, called relative-perfectness
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persistence-perfectness
relative-perfectness
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persistent homology. The first result about a persistence-perfect gradient vector-field is that the number of its critical cells yield inequalities bounding the
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
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, as a sort of Morse inequalities for multi-parameter persistence. The second result is that, at least in low dimensions, persistence-perfect gradient vector-fields not only exist but can be constructed by an algorithm
from above and below, via the number of critical cells. Our last result is the proof that existing algorithms
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. These results show a link between multi-parameter persistence and discrete Morse theory that can be leveraged for a better understanding of the former.
allow for efficient computability over simplicial complexes up to dimension 2.