3,560 research outputs found
Persistence for Circle Valued Maps
We study circle valued maps and consider the persistence of the homology of
their fibers. The outcome is a finite collection of computable invariants which
answer the basic questions on persistence and in addition encode the topology
of the source space and its relevant subspaces. Unlike persistence of real
valued maps, circle valued maps enjoy a different class of invariants called
Jordan cells in addition to bar codes. We establish a relation between the
homology of the source space and of its relevant subspaces with these
invariants and provide a new algorithm to compute these invariants from an
input matrix that encodes a circle valued map on an input simplicial complex.Comment: A complete algorithm to compute barcodes and Jordan cells is provided
in this version. The paper is accepted in in the journal Discrete &
Computational Geometry. arXiv admin note: text overlap with arXiv:1210.3092
by other author
Persistent Cohomology and Circular Coordinates
Nonlinear dimensionality reduction (NLDR) algorithms such as Isomap, LLE and
Laplacian Eigenmaps address the problem of representing high-dimensional
nonlinear data in terms of low-dimensional coordinates which represent the
intrinsic structure of the data. This paradigm incorporates the assumption that
real-valued coordinates provide a rich enough class of functions to represent
the data faithfully and efficiently. On the other hand, there are simple
structures which challenge this assumption: the circle, for example, is
one-dimensional but its faithful representation requires two real coordinates.
In this work, we present a strategy for constructing circle-valued functions on
a statistical data set. We develop a machinery of persistent cohomology to
identify candidates for significant circle-structures in the data, and we use
harmonic smoothing and integration to obtain the circle-valued coordinate
functions themselves. We suggest that this enriched class of coordinate
functions permits a precise NLDR analysis of a broader range of realistic data
sets.Comment: 10 pages, 7 figures. To appear in the proceedings of the ACM
Symposium on Computational Geometry 200
Multidimensional persistent homology is stable
Multidimensional persistence studies topological features of shapes by
analyzing the lower level sets of vector-valued functions. The rank invariant
completely determines the multidimensional analogue of persistent homology
groups. We prove that multidimensional rank invariants are stable with respect
to function perturbations. More precisely, we construct a distance between rank
invariants such that small changes of the function imply only small changes of
the rank invariant. This result can be obtained by assuming the function to be
just continuous. Multidimensional stability opens the way to a stable shape
comparison methodology based on multidimensional persistence.Comment: 14 pages, 3 figure
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