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UMAP: Uniform Manifold Approximation and Projection
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.Expand
Ripser.py: A Lean Persistent Homology Library for Python
MLWave/kepler-mapper: 186f
Robotic simulation of dynamic plume tracking by Unmanned Surface Vessels
TLDR
A probabilistic Lagrangian environmental model is used that can capture both the time-averaged, idealized structure and the instantaneous, realistic structure of a dynamic plume. Expand
Kepler Mapper: A flexible Python implementation of the Mapper algorithm
TLDR
The Mapper algorithm is developed to facilitate graphical exploration of topological data structures and its use in multiple domains, including political science, biology, and sports analytics. Expand
Jaccard Filtration and Stable Paths in the Mapper
TLDR
The framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths, and shows that the stable paths in the cover filTration provide improved explanations of relationships between subpopulations of images. Expand
Interactive Machine Learning Heuristics
End-user interaction with machine learning based systems will result in new usability challenges for the field of human computer interaction. Machine learning algorithms are often complicated to theExpand
Steinhaus Filtration and Stable Paths in the Mapper
TLDR
A new filtration built from a single cover based on a generalized Steinhaus distance, which is a generalization of Jaccard distance is defined, which proves a stability result: the cover filtrations of two covers are interleaved. Expand
Stitch Fix for Mapper
Mapper is one of the main tools in topological data analysis (TDA) and visualization used for the study of multivariate data [3]. It takes as input a multivariate function and produces a summary ofExpand
Stitch Fix for Mapper and Information Gains
TLDR
This paper investigates a method for stitching a pair of univariate mappers together into a bivariate mapper and study topological notions of information gains during such a process and provides implementations that visualize such information gains. Expand
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