568 research outputs found
Supervised Learning with Indefinite Topological Kernels
Topological Data Analysis (TDA) is a recent and growing branch of statistics
devoted to the study of the shape of the data. In this work we investigate the
predictive power of TDA in the context of supervised learning. Since
topological summaries, most noticeably the Persistence Diagram, are typically
defined in complex spaces, we adopt a kernel approach to translate them into
more familiar vector spaces. We define a topological exponential kernel, we
characterize it, and we show that, despite not being positive semi-definite, it
can be successfully used in regression and classification tasks
HOMFLYPT homology of Coxeter links
A Coxeter link is a closure of a product of two braids, one being a
quasi-Coxeter element and the other being a product of partial full twists.
This class of links includes torus knots and torus links
. We identify the knot homology of a Coxeter link with the space of
sections of a particular line bundle on a natural generalization of the
punctual locus inside the flag Hilbert scheme of points in .Comment: 23 pages, few misprints are corrected, abstract is expande
Interpretable statistics for complex modelling: quantile and topological learning
As the complexity of our data increased exponentially in the last decades, so has our
need for interpretable features. This thesis revolves around two paradigms to approach
this quest for insights.
In the first part we focus on parametric models, where the problem of interpretability
can be seen as a “parametrization selection”. We introduce a quantile-centric
parametrization and we show the advantages of our proposal in the context of regression,
where it allows to bridge the gap between classical generalized linear (mixed)
models and increasingly popular quantile methods.
The second part of the thesis, concerned with topological learning, tackles the
problem from a non-parametric perspective. As topology can be thought of as a way
of characterizing data in terms of their connectivity structure, it allows to represent
complex and possibly high dimensional through few features, such as the number of
connected components, loops and voids. We illustrate how the emerging branch of
statistics devoted to recovering topological structures in the data, Topological Data
Analysis, can be exploited both for exploratory and inferential purposes with a special
emphasis on kernels that preserve the topological information in the data.
Finally, we show with an application how these two approaches can borrow strength
from one another in the identification and description of brain activity through fMRI
data from the ABIDE project
- …