3,504 research outputs found
Kernel functions based on triplet comparisons
Given only information in the form of similarity triplets "Object A is more
similar to object B than to object C" about a data set, we propose two ways of
defining a kernel function on the data set. While previous approaches construct
a low-dimensional Euclidean embedding of the data set that reflects the given
similarity triplets, we aim at defining kernel functions that correspond to
high-dimensional embeddings. These kernel functions can subsequently be used to
apply any kernel method to the data set
Tree models for difference and change detection in a complex environment
A new family of tree models is proposed, which we call "differential trees."
A differential tree model is constructed from multiple data sets and aims to
detect distributional differences between them. The new methodology differs
from the existing difference and change detection techniques in its
nonparametric nature, model construction from multiple data sets, and
applicability to high-dimensional data. Through a detailed study of an arson
case in New Zealand, where an individual is known to have been laying
vegetation fires within a certain time period, we illustrate how these models
can help detect changes in the frequencies of event occurrences and uncover
unusual clusters of events in a complex environment.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS548 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Nonparametric Edge Detection in Speckled Imagery
We address the issue of edge detection in Synthetic Aperture Radar imagery.
In particular, we propose nonparametric methods for edge detection, and
numerically compare them to an alternative method that has been recently
proposed in the literature. Our results show that some of the proposed methods
display superior results and are computationally simpler than the existing
method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio
DAISEE: Dataset for Affective States in E-Learning Environments
Extracting and understanding a ective states of subjects
through analysis of face videos is of high consequence to advance the
levels of interaction in human-computer interfaces. This paper aims to
highlight vision-related tasks focused on understanding \reactions" of
subjects to presented content which has not been largely studied by the
vision community in comparison to other emotions. To facilitate future
study in this eld, we present an e ort in collecting DAiSEE, a free to
use large-scale dataset using crowd annotation, that not only simulates
a real world setting for e-learning environments, but also captures the
interpretability issues of such a ective states by human annotators. In
addition to the dataset, we present benchmark results based on stan-
dard baseline methods and vote aggregation strategies, thus providing a
springboard for further research
Computational fact checking from knowledge networks
Traditional fact checking by expert journalists cannot keep up with the
enormous volume of information that is now generated online. Computational fact
checking may significantly enhance our ability to evaluate the veracity of
dubious information. Here we show that the complexities of human fact checking
can be approximated quite well by finding the shortest path between concept
nodes under properly defined semantic proximity metrics on knowledge graphs.
Framed as a network problem this approach is feasible with efficient
computational techniques. We evaluate this approach by examining tens of
thousands of claims related to history, entertainment, geography, and
biographical information using a public knowledge graph extracted from
Wikipedia. Statements independently known to be true consistently receive
higher support via our method than do false ones. These findings represent a
significant step toward scalable computational fact-checking methods that may
one day mitigate the spread of harmful misinformation
- …