J
Jan Reininghaus
Researcher at Institute of Science and Technology Austria
Publications - 34
Citations - 1597
Jan Reininghaus is an academic researcher from Institute of Science and Technology Austria. The author has contributed to research in topics: Discrete Morse theory & Persistent homology. The author has an hindex of 18, co-authored 34 publications receiving 1393 citations. Previous affiliations of Jan Reininghaus include Zuse Institute Berlin & Humboldt University of Berlin.
Papers
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Proceedings ArticleDOI
A stable multi-scale kernel for topological machine learning
TL;DR: In this paper, a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data, is proposed for 3D shape classification/retrieval and texture recognition.
Proceedings ArticleDOI
SHREC 2011: robust feature detection and description benchmark
Edmond Boyer,Alexander M. Bronstein,Michael M. Bronstein,Benjamin Bustos,T. Darom,Radu Horaud,Ingrid Hotz,Yosi Keller,Johannes Keustermans,Artiom Kovnatsky,Roee Litman,Jan Reininghaus,Ivan Sipiran,Dirk Smeets,Paul Suetens,Dirk Vandermeulen,Andrei Zaharescu,Valentin Zobel +17 more
TL;DR: A benchmark that simulates the feature detection and description stages of feature-based shape retrieval algorithms under a wide variety of transformations is presented.
Journal ArticleDOI
Phat - Persistent Homology Algorithms Toolbox
TL;DR: This work provides numerous different reduction strategies as well as data types to store and manipulate the boundary matrix and compares the different combinations through extensive experimental evaluation and identifies optimization techniques that work well in practical situations.
Book ChapterDOI
Distributed computation of persistent homology
TL;DR: It is demonstrated that a simple adaption of the standard reduction algorithm leads to a variant for distributed systems that at least compensates for the overhead caused by communication between nodes, and often even speeds up the computation compared to sequential and even parallel shared memory algorithms.
Book ChapterDOI
Clear and Compress: Computing Persistent Homology in Chunks
TL;DR: In this paper, a parallel algorithm for computing the persistent homology of a filtered chain complex is presented, which differs from the commonly used reduction algorithm by first computing persistence pairs within local chunks, then simplifying the unpaired columns, and finally applying standard reduction on the simplified matrix.