This paper extends the key concept of persistence within Topological Data Analysis (TDA) in a new direction. TDA quantifies topological shapes hidden in unorganized data such as clouds of unordered points. In the 0-dimensional case the distance-based persistence is determined by a single-linkage (SL) clustering of a finite set in a metric space. Equivalently, the 0D persistence captures only edge-lengths of a Minimum Spanning Tree (MST). Both SL dendrogram and MST are unstable under perturbations of points. We define the new stable-under-noise mergegram, which outperforms previous isometry invariants on a classification of point clouds by PersLay
The analysis of data sets mathematically representable as finite metric spaces plays a significant r...
International audienceComputational topology has recently seen an important development toward data ...
Persistent homology is a methodology central to topological data analysis that extracts and summariz...
This paper extends the key concept of persistence within Topological Data Analysis (TDA) in a new di...
In this work we define a novel metric structure to work with functions defined on merge trees. The m...
The rising field of Topological Data Analysis (TDA) provides a new approach to learning from data th...
Acknowledgments We gratefully acknowledge Roel Neggers for providing the DALES simulation data. JLS ...
Data has shape and that shape is important. This is the anthem of Topological Data Analysis (TDA) as...
International audienceIn the last decade, there has been increasing interest in topological data ana...
Persistence is a theory for Topological Data Analysis based on analyzing the scale at whichtopologic...
We present a novel clustering algorithm that combines a mode-seeking phase with a cluster merging ph...
<p>In this thesis, we explore techniques in statistics and persistent homology, which detect feature...
Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine le...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
Persistence landscapes are functional summaries of persistence diagrams designed to enable analysis ...
The analysis of data sets mathematically representable as finite metric spaces plays a significant r...
International audienceComputational topology has recently seen an important development toward data ...
Persistent homology is a methodology central to topological data analysis that extracts and summariz...
This paper extends the key concept of persistence within Topological Data Analysis (TDA) in a new di...
In this work we define a novel metric structure to work with functions defined on merge trees. The m...
The rising field of Topological Data Analysis (TDA) provides a new approach to learning from data th...
Acknowledgments We gratefully acknowledge Roel Neggers for providing the DALES simulation data. JLS ...
Data has shape and that shape is important. This is the anthem of Topological Data Analysis (TDA) as...
International audienceIn the last decade, there has been increasing interest in topological data ana...
Persistence is a theory for Topological Data Analysis based on analyzing the scale at whichtopologic...
We present a novel clustering algorithm that combines a mode-seeking phase with a cluster merging ph...
<p>In this thesis, we explore techniques in statistics and persistent homology, which detect feature...
Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine le...
In recent years, persistent homology techniques have been used to study data and dynamical systems. ...
Persistence landscapes are functional summaries of persistence diagrams designed to enable analysis ...
The analysis of data sets mathematically representable as finite metric spaces plays a significant r...
International audienceComputational topology has recently seen an important development toward data ...
Persistent homology is a methodology central to topological data analysis that extracts and summariz...