# Persistence Flamelets: multiscale Persistent Homology for kernel density exploration

@article{Padellini2017PersistenceFM, title={Persistence Flamelets: multiscale Persistent Homology for kernel density exploration}, author={Tullia Padellini and Pierpaolo Brutti}, journal={arXiv: Machine Learning}, year={2017} }

In recent years there has been noticeable interest in the study of the "shape of data". Among the many ways a "shape" could be defined, topology is the most general one, as it describes an object in terms of its connectivity structure: connected components (topological features of dimension 0), cycles (features of dimension 1) and so on. There is a growing number of techniques, generally denoted as Topological Data Analysis, aimed at estimating topological invariants of a fixed object; when we…

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