12,251 research outputs found
ICA-based sparse feature recovery from fMRI datasets
Spatial Independent Components Analysis (ICA) is increasingly used in the
context of functional Magnetic Resonance Imaging (fMRI) to study cognition and
brain pathologies. Salient features present in some of the extracted
Independent Components (ICs) can be interpreted as brain networks, but the
segmentation of the corresponding regions from ICs is still ill-controlled.
Here we propose a new ICA-based procedure for extraction of sparse features
from fMRI datasets. Specifically, we introduce a new thresholding procedure
that controls the deviation from isotropy in the ICA mixing model. Unlike
current heuristics, our procedure guarantees an exact, possibly conservative,
level of specificity in feature detection. We evaluate the sensitivity and
specificity of the method on synthetic and fMRI data and show that it
outperforms state-of-the-art approaches
Visual and interactive exploration of point data
Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine
scales of resolution. For instance, socio-economic attributes are commonly assigned to
UPC. Hence, they can be represented as points and observable at the postcode level.
Using UPC as a common field allows the concatenation of variables from disparate data
sources that can potentially support sophisticated spatial analysis. However, visualising
UPC in urban areas has at least three limitations. First, at small scales UPC occurrences
can be very dense making their visualisation as points difficult. On the other hand,
patterns in the associated attribute values are often hardly recognisable at large scales.
Secondly, UPC can be used as a common field to allow the concatenation of highly
multivariate data sets with an associated postcode. Finally, socio-economic variables
assigned to UPC (such as the ones used here) can be non-Normal in their distributions
as a result of a large presence of zero values and high variances which constrain their
analysis using traditional statistics.
This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system
developed to visually explore point data. Various well-known visualisation techniques
were implemented to enable their interactive and dynamic interrogation. PVT provides
multiple representations of point data to facilitate the understanding of the relations
between attributes or variables as well as their spatial characteristics. Brushing between
alternative views is used to link several representations of a single attribute, as well as
to simultaneously explore more than one variable. PVT’s functionality shows how the
use of visual techniques embedded in an interactive environment enable the exploration
of large amounts of multivariate point data
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
Exact asymptotic distribution of change-point mle for change in the mean of Gaussian sequences
We derive exact computable expressions for the asymptotic distribution of the
change-point mle when a change in the mean occurred at an unknown point of a
sequence of time-ordered independent Gaussian random variables. The derivation,
which assumes that nuisance parameters such as the amount of change and
variance are known, is based on ladder heights of Gaussian random walks hitting
the half-line. We then show that the exact distribution easily extends to the
distribution of the change-point mle when a change occurs in the mean vector of
a multivariate Gaussian process. We perform simulations to examine the accuracy
of the derived distribution when nuisance parameters have to be estimated as
well as robustness of the derived distribution to deviations from Gaussianity.
Through simulations, we also compare it with the well-known conditional
distribution of the mle, which may be interpreted as a Bayesian solution to the
change-point problem. Finally, we apply the derived methodology to monthly
averages of water discharges of the Nacetinsky creek, Germany.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS294 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
t-Exponential Memory Networks for Question-Answering Machines
Recent advances in deep learning have brought to the fore models that can
make multiple computational steps in the service of completing a task; these
are capable of describ- ing long-term dependencies in sequential data. Novel
recurrent attention models over possibly large external memory modules
constitute the core mechanisms that enable these capabilities. Our work
addresses learning subtler and more complex underlying temporal dynamics in
language modeling tasks that deal with sparse sequential data. To this end, we
improve upon these recent advances, by adopting concepts from the field of
Bayesian statistics, namely variational inference. Our proposed approach
consists in treating the network parameters as latent variables with a prior
distribution imposed over them. Our statistical assumptions go beyond the
standard practice of postulating Gaussian priors. Indeed, to allow for handling
outliers, which are prevalent in long observed sequences of multivariate data,
multivariate t-exponential distributions are imposed. On this basis, we proceed
to infer corresponding posteriors; these can be used for inference and
prediction at test time, in a way that accounts for the uncertainty in the
available sparse training data. Specifically, to allow for our approach to best
exploit the merits of the t-exponential family, our method considers a new
t-divergence measure, which generalizes the concept of the Kullback-Leibler
divergence. We perform an extensive experimental evaluation of our approach,
using challenging language modeling benchmarks, and illustrate its superiority
over existing state-of-the-art techniques
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Semantic Image Retrieval via Active Grounding of Visual Situations
We describe a novel architecture for semantic image retrieval---in
particular, retrieval of instances of visual situations. Visual situations are
concepts such as "a boxing match," "walking the dog," "a crowd waiting for a
bus," or "a game of ping-pong," whose instantiations in images are linked more
by their common spatial and semantic structure than by low-level visual
similarity. Given a query situation description, our architecture---called
Situate---learns models capturing the visual features of expected objects as
well the expected spatial configuration of relationships among objects. Given a
new image, Situate uses these models in an attempt to ground (i.e., to create a
bounding box locating) each expected component of the situation in the image
via an active search procedure. Situate uses the resulting grounding to compute
a score indicating the degree to which the new image is judged to contain an
instance of the situation. Such scores can be used to rank images in a
collection as part of a retrieval system. In the preliminary study described
here, we demonstrate the promise of this system by comparing Situate's
performance with that of two baseline methods, as well as with a related
semantic image-retrieval system based on "scene graphs.
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