The natural pseudo-distance of spaces endowed with filtering functions is precious for shape classification and retrieval; its optimal estimate coming from persistence diagrams is the bottleneck distance, which unfortunately suffers from combinatorial explosion. A possible algebraic representation of persistence diagrams is offered by complex polynomials; since far polynomials represent far persistence diagrams, a fast comparison of the coefficient vectors can reduce the size of the database to be classified by the bottleneck distance. This article explores experimentally three transformations from diagrams to polynomials and three distances between the complex vectors of coefficients
2021 Spring.Includes bibliographical references.Persistent homology often begins with a filtered sim...
The theory of multidimensional persistent homology was initially developed in the discrete setting, ...
Persistent homology is a tool that can be employed to summarize the shape of data by quantifying hom...
none2noThe natural pseudo-distance of spaces endowed with filtering functions is precious for shape...
Persistence diagrams, combining geometry and topology for an effective shape description used in pat...
Persistence is a theory for Topological Data Analysis based on analyzing the scale at whichtopologic...
The ability to perform shape retrieval based not only on full similarity, but also partial similarit...
Abstract. This paper deals with the concepts of persistence diagrams and matching distance. They are...
The extended persistence diagram is an invariant of piecewise linear functions, introduced by Cohen-...
This paper deals with the concepts of persistence diagram and matching distance. These are two of th...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
Data has shape and that shape is important. This is the anthem of Topological Data Analysis (TDA) as...
Recent years have witnessed a tremendous growth using topological summaries, especially the persiste...
A point cloud can be endowed with a topological structure by constructing a simplicial complex using...
The ability to perform not only global matching but also partial matching is in-vestigated in comput...
2021 Spring.Includes bibliographical references.Persistent homology often begins with a filtered sim...
The theory of multidimensional persistent homology was initially developed in the discrete setting, ...
Persistent homology is a tool that can be employed to summarize the shape of data by quantifying hom...
none2noThe natural pseudo-distance of spaces endowed with filtering functions is precious for shape...
Persistence diagrams, combining geometry and topology for an effective shape description used in pat...
Persistence is a theory for Topological Data Analysis based on analyzing the scale at whichtopologic...
The ability to perform shape retrieval based not only on full similarity, but also partial similarit...
Abstract. This paper deals with the concepts of persistence diagrams and matching distance. They are...
The extended persistence diagram is an invariant of piecewise linear functions, introduced by Cohen-...
This paper deals with the concepts of persistence diagram and matching distance. These are two of th...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
Data has shape and that shape is important. This is the anthem of Topological Data Analysis (TDA) as...
Recent years have witnessed a tremendous growth using topological summaries, especially the persiste...
A point cloud can be endowed with a topological structure by constructing a simplicial complex using...
The ability to perform not only global matching but also partial matching is in-vestigated in comput...
2021 Spring.Includes bibliographical references.Persistent homology often begins with a filtered sim...
The theory of multidimensional persistent homology was initially developed in the discrete setting, ...
Persistent homology is a tool that can be employed to summarize the shape of data by quantifying hom...