[PDF][PDF] A roadmap for the computation of persistent homology
Persistent homology (PH) is a method used in topological data analysis (TDA) to study
qualitative features of data that persist across multiple scales. It is robust to perturbations of …
qualitative features of data that persist across multiple scales. It is robust to perturbations of …
Persistent-homology-based machine learning: a survey and a comparative study
A suitable feature representation that can both preserve the data intrinsic information and
reduce data complexity and dimensionality is key to the performance of machine learning …
reduce data complexity and dimensionality is key to the performance of machine learning …
[HTML][HTML] Topological deep learning: a review of an emerging paradigm
Topological deep learning (TDL) is an emerging area that combines the principles of
Topological data analysis (TDA) with deep learning techniques. TDA provides insight into …
Topological data analysis (TDA) with deep learning techniques. TDA provides insight into …
A topological regularizer for classifiers via persistent homology
Regularization plays a crucial role in supervised learning. Most existing methods enforce a
global regularization in a structure agnostic manner. In this paper, we initiate a new direction …
global regularization in a structure agnostic manner. In this paper, we initiate a new direction …
[HTML][HTML] Topological data analysis of single-trial electroencephalographic signals
Epilepsy is a neurological disorder that can negatively affect the visual, audial and motor
functions of the human brain. Statistical analysis of neurophysiological recordings, such as …
functions of the human brain. Statistical analysis of neurophysiological recordings, such as …
Persistent homology of geospatial data: A case study with voting
A crucial step in the analysis of persistent homology is the transformation of data into an
appropriate topological object (which, in our case, is a simplicial complex). Software …
appropriate topological object (which, in our case, is a simplicial complex). Software …
Approximating continuous functions on persistence diagrams using template functions
The persistence diagram is an increasingly useful tool from Topological Data Analysis, but
its use alongside typical machine learning techniques requires mathematical finesse. The …
its use alongside typical machine learning techniques requires mathematical finesse. The …
Adaptive partitioning for template functions on persistence diagrams
S Tymochko, E Munch… - 2019 18th IEEE …, 2019 - ieeexplore.ieee.org
As the field of Topological Data Analysis continues to show success in theory and in
applications, there has been increasing interest in using tools from this field with methods for …
applications, there has been increasing interest in using tools from this field with methods for …
Training-Time Attacks against k-Nearest Neighbors
A Vartanian, W Rosenbaum, S Alfeld - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Nearest neighbor-based methods are commonly used for classification tasks and as
subroutines of other data-analysis methods. An attacker with the capability of inserting their …
subroutines of other data-analysis methods. An attacker with the capability of inserting their …
Geometric metrics for topological representations
In this chapter, we present an overview of recent techniques from the emerging area of
topological data analysis (TDA), with a focus on machine-learning applications. TDA …
topological data analysis (TDA), with a focus on machine-learning applications. TDA …