We present a dynamic data structure for maintaining the persistent homology of a time series of real numbers. The data structure supports local operations, including the insertion and deletion of an item and the cutting and concatenating of lists, each in time $O(\log n + k)$, in which $n$ counts the critical items and $k$ the changes in the augmented persistence diagram. To achieve this, we design a tailor-made tree structure with an unconventional representation, referred to as banana tree, which may be useful in its own right.Comment: To appear at SODA 202
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...
Persistent homology barcodes and diagrams are a cornerstone of topological data analysis. Widely use...
In this position paper, we present a brief overview of the ways topological tools, in particular per...
In various applications of data classification and clustering problems, multi-parameter analysis is ...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
Persistence is a fairly well established tool in topological data analysis used to infer geometric i...
We set up the theory for a distributed algorithm for computing persistent homology. For this purpose...
Harnessing the power of data has been a driving force for computing in recently years. However, the ...
Persistent homology is a methodology central to topological data analysis that extracts and summariz...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
The topological data analysis studies the shape of a space at multiple scales. Its main tool is pers...
Copyright c © 2008 by Dmitriy Morozov In this thesis we explore and extend the theory of persistent ...
Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data th...
Persistent homology (PH) is a method used in topological data analysis (TDA) to study qualitative fe...
Recent innovations in combinatorial dynamical systems permit them to be studied with algorithmic met...
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...
Persistent homology barcodes and diagrams are a cornerstone of topological data analysis. Widely use...
In this position paper, we present a brief overview of the ways topological tools, in particular per...
In various applications of data classification and clustering problems, multi-parameter analysis is ...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
Persistence is a fairly well established tool in topological data analysis used to infer geometric i...
We set up the theory for a distributed algorithm for computing persistent homology. For this purpose...
Harnessing the power of data has been a driving force for computing in recently years. However, the ...
Persistent homology is a methodology central to topological data analysis that extracts and summariz...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
The topological data analysis studies the shape of a space at multiple scales. Its main tool is pers...
Copyright c © 2008 by Dmitriy Morozov In this thesis we explore and extend the theory of persistent ...
Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data th...
Persistent homology (PH) is a method used in topological data analysis (TDA) to study qualitative fe...
Recent innovations in combinatorial dynamical systems permit them to be studied with algorithmic met...
Abstract. We approach the problem of the computation of persistent homology for large datasets by a ...
Persistent homology barcodes and diagrams are a cornerstone of topological data analysis. Widely use...
In this position paper, we present a brief overview of the ways topological tools, in particular per...