4,324 research outputs found
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability
distributions of random variables. The method uses reproducing kernel Hilbert
space representations of probability distributions, and it is applicable to all
operations which can be applied to points drawn from the respective
distributions. We refer to our approach as {\em kernel probabilistic
programming}. We illustrate it on synthetic data, and show how it can be used
for nonparametric structural equation models, with an application to causal
inference
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
The method is embedded into a kernel regression machine that can model general
nonlinear functions, not being a priori limited to additive models. This is the
first kernel-based variable selection method applicable to large datasets. It
sidesteps the typical poor scaling properties of kernel methods by mapping the
inputs into a relatively low-dimensional space of random features. The
algorithm discovers the variables relevant for the regression task together
with learning the prediction model through learning the appropriate nonlinear
random feature maps. We demonstrate the outstanding performance of our method
on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201
Evolutionary distances in the twilight zone -- a rational kernel approach
Phylogenetic tree reconstruction is traditionally based on multiple sequence
alignments (MSAs) and heavily depends on the validity of this information
bottleneck. With increasing sequence divergence, the quality of MSAs decays
quickly. Alignment-free methods, on the other hand, are based on abstract
string comparisons and avoid potential alignment problems. However, in general
they are not biologically motivated and ignore our knowledge about the
evolution of sequences. Thus, it is still a major open question how to define
an evolutionary distance metric between divergent sequences that makes use of
indel information and known substitution models without the need for a multiple
alignment. Here we propose a new evolutionary distance metric to close this
gap. It uses finite-state transducers to create a biologically motivated
similarity score which models substitutions and indels, and does not depend on
a multiple sequence alignment. The sequence similarity score is defined in
analogy to pairwise alignments and additionally has the positive semi-definite
property. We describe its derivation and show in simulation studies and
real-world examples that it is more accurate in reconstructing phylogenies than
competing methods. The result is a new and accurate way of determining
evolutionary distances in and beyond the twilight zone of sequence alignments
that is suitable for large datasets.Comment: to appear in PLoS ON
- …