Add to ORKGI will discuss how topological summaries can be used to model various shapes as well as other high-dimensional data. I will talk about geometric and statistical properties of the space of summaries and how these summaries are the result of various filtrations. I will then discuss why multi-parameter persistence would be a very nice tool for these problems. The first half of the talk will be a review and I will focus on some of my research in the second half.Non UBCUnreviewedAuthor affiliation: Duke UniversityFacult
In this paper we introduce a statistic, the persistent homology transform (PHT), to model surfaces i...
Computational topology has recently known an important development toward data analysis, giving birt...
Summary. I develop algebraic-topological theories, algorithms and software for the analysis of non-l...
I will discuss how topological summaries can be used to model various shapes as well as other high-d...
Abstract. We define a new topological summary for data that we call the persistence land-scape. In c...
We explore Persistence Theory in its full generality. As a particular instance, we first discuss one...
The utilization of statistical methods an their applications within the new field of study known as ...
Topological Data Analysis (TDA) is a novel statistical technique, particularly powerful for the anal...
This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional...
Until very recently, topological data analysis and topological inference methods mostlyrelied on det...
Topological data analysis (or TDA for short) consists in a set of methods aiming to extract topologi...
Topological data analysis (TDA) is an approach to the analysis of datasets using techniques from top...
Modelling topological properties of the spatial relationship between objects, known as the extit{top...
Computational topology has recently known an important development toward data analysis, giving birt...
Persistent homology is a method for probing topological properties of point clouds and functions. Th...
In this paper we introduce a statistic, the persistent homology transform (PHT), to model surfaces i...
Computational topology has recently known an important development toward data analysis, giving birt...
Summary. I develop algebraic-topological theories, algorithms and software for the analysis of non-l...
I will discuss how topological summaries can be used to model various shapes as well as other high-d...
Abstract. We define a new topological summary for data that we call the persistence land-scape. In c...
We explore Persistence Theory in its full generality. As a particular instance, we first discuss one...
The utilization of statistical methods an their applications within the new field of study known as ...
Topological Data Analysis (TDA) is a novel statistical technique, particularly powerful for the anal...
This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional...
Until very recently, topological data analysis and topological inference methods mostlyrelied on det...
Topological data analysis (or TDA for short) consists in a set of methods aiming to extract topologi...
Topological data analysis (TDA) is an approach to the analysis of datasets using techniques from top...
Modelling topological properties of the spatial relationship between objects, known as the extit{top...
Computational topology has recently known an important development toward data analysis, giving birt...
Persistent homology is a method for probing topological properties of point clouds and functions. Th...
In this paper we introduce a statistic, the persistent homology transform (PHT), to model surfaces i...
Computational topology has recently known an important development toward data analysis, giving birt...
Summary. I develop algebraic-topological theories, algorithms and software for the analysis of non-l...