3,897 research outputs found
Unsupervised learning of human motion
An unsupervised learning algorithm that can obtain a probabilistic model of an object composed of a collection of parts (a moving human body in our examples) automatically from unlabeled training data is presented. The training data include both useful "foreground" features as well as features that arise from irrelevant background clutter - the correspondence between parts and detected features is unknown. The joint probability density function of the parts is represented by a mixture of decomposable triangulated graphs which allow for fast detection. To learn the model structure as well as model parameters, an EM-like algorithm is developed where the labeling of the data (part assignments) is treated as hidden variables. The unsupervised learning technique is not limited to decomposable triangulated graphs. The efficiency and effectiveness of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled image sequences, and testing the learned models on a variety of sequences
Coalescent histories for lodgepole species trees
Coalescent histories are combinatorial structures that describe for a given
gene tree and species tree the possible lists of branches of the species tree
on which the gene tree coalescences take place. Properties of the number of
coalescent histories for gene trees and species trees affect a variety of
probabilistic calculations in mathematical phylogenetics. Exact and asymptotic
evaluations of the number of coalescent histories, however, are known only in a
limited number of cases. Here we introduce a particular family of species
trees, the \emph{lodgepole} species trees , in which
tree has taxa. We determine the number of coalescent
histories for the lodgepole species trees, in the case that the gene tree
matches the species tree, showing that this number grows with in the
number of taxa . This computation demonstrates the existence of tree
families in which the growth in the number of coalescent histories is faster
than exponential. Further, it provides a substantial improvement on the lower
bound for the ratio of the largest number of matching coalescent histories to
the smallest number of matching coalescent histories for trees with taxa,
increasing a previous bound of
to . We discuss the implications of our
enumerative results for phylogenetic computations
Graph kernels between point clouds
Point clouds are sets of points in two or three dimensions. Most kernel
methods for learning on sets of points have not yet dealt with the specific
geometrical invariances and practical constraints associated with point clouds
in computer vision and graphics. In this paper, we present extensions of graph
kernels for point clouds, which allow to use kernel methods for such ob jects
as shapes, line drawings, or any three-dimensional point clouds. In order to
design rich and numerically efficient kernels with as few free parameters as
possible, we use kernels between covariance matrices and their factorizations
on graphical models. We derive polynomial time dynamic programming recursions
and present applications to recognition of handwritten digits and Chinese
characters from few training examples
Computational Complexity of the Interleaving Distance
The interleaving distance is arguably the most prominent distance measure in
topological data analysis. In this paper, we provide bounds on the
computational complexity of determining the interleaving distance in several
settings. We show that the interleaving distance is NP-hard to compute for
persistence modules valued in the category of vector spaces. In the specific
setting of multidimensional persistent homology we show that the problem is at
least as hard as a matrix invertibility problem. Furthermore, this allows us to
conclude that the interleaving distance of interval decomposable modules
depends on the characteristic of the field. Persistence modules valued in the
category of sets are also studied. As a corollary, we obtain that the
isomorphism problem for Reeb graphs is graph isomorphism complete.Comment: Discussion related to the characteristic of the field added. Paper
accepted to the 34th International Symposium on Computational Geometr
Quillen homology for operads via Gr\"obner bases
The main goal of this paper is to present a way to compute Quillen homology
of operads. The key idea is to use the notion of a shuffle operad we introduced
earlier; this allows to compute, for a symmetric operad, the homology classes
and the shape of the differential in its minimal model, although does not give
an insight on the symmetric groups action on the homology. Our approach goes in
several steps. First, we regard our symmetric operad as a shuffle operad, which
allows to compute its Gr\"obner basis. Next, we define a combinatorial
resolution for the "monomial replacement" of each shuffle operad (provided by
the Gr\"obner bases theory). Finally, we explain how to "deform" the
differential to handle every operad with a Gr\"obner basis, and find explicit
representatives of Quillen homology classes for a large class of operads. We
also present various applications, including a new proof of Hoffbeck's PBW
criterion, a proof of Koszulness for a class of operads coming from commutative
algebras, and a homology computation for the operads of Batalin-Vilkovisky
algebras and of Rota-Baxter algebras.Comment: 41 pages, this paper supersedes our previous preprint
arXiv:0912.4895. Final version, to appear in Documenta Mat
- …