17,026 research outputs found
Polyhedral computational geometry for averaging metric phylogenetic trees
This paper investigates the computational geometry relevant to calculations
of the Frechet mean and variance for probability distributions on the
phylogenetic tree space of Billera, Holmes and Vogtmann, using the theory of
probability measures on spaces of nonpositive curvature developed by Sturm. We
show that the combinatorics of geodesics with a specified fixed endpoint in
tree space are determined by the location of the varying endpoint in a certain
polyhedral subdivision of tree space. The variance function associated to a
finite subset of tree space has a fixed algebraic formula within
each cell of the corresponding subdivision, and is continuously differentiable
in the interior of each orthant of tree space. We use this subdivision to
establish two iterative methods for producing sequences that converge to the
Frechet mean: one based on Sturm's Law of Large Numbers, and another based on
descent algorithms for finding optima of smooth functions on convex polyhedra.
We present properties and biological applications of Frechet means and extend
our main results to more general globally nonpositively curved spaces composed
of Euclidean orthants.Comment: 43 pages, 6 figures; v2: fixed typos, shortened Sections 1 and 5,
added counter example for polyhedrality of vistal subdivision in general
CAT(0) cubical complexes; v1: 43 pages, 5 figure
Narrowing the Gap: Random Forests In Theory and In Practice
Despite widespread interest and practical use, the theoretical properties of
random forests are still not well understood. In this paper we contribute to
this understanding in two ways. We present a new theoretically tractable
variant of random regression forests and prove that our algorithm is
consistent. We also provide an empirical evaluation, comparing our algorithm
and other theoretically tractable random forest models to the random forest
algorithm used in practice. Our experiments provide insight into the relative
importance of different simplifications that theoreticians have made to obtain
tractable models for analysis.Comment: Under review by the International Conference on Machine Learning
(ICML) 201
- β¦