75,539 research outputs found
Graph Estimation From Multi-attribute Data
Many real world network problems often concern multivariate nodal attributes
such as image, textual, and multi-view feature vectors on nodes, rather than
simple univariate nodal attributes. The existing graph estimation methods built
on Gaussian graphical models and covariance selection algorithms can not handle
such data, neither can the theories developed around such methods be directly
applied. In this paper, we propose a new principled framework for estimating
graphs from multi-attribute data. Instead of estimating the partial correlation
as in current literature, our method estimates the partial canonical
correlations that naturally accommodate complex nodal features.
Computationally, we provide an efficient algorithm which utilizes the
multi-attribute structure. Theoretically, we provide sufficient conditions
which guarantee consistent graph recovery. Extensive simulation studies
demonstrate performance of our method under various conditions. Furthermore, we
provide illustrative applications to uncovering gene regulatory networks from
gene and protein profiles, and uncovering brain connectivity graph from
functional magnetic resonance imaging data.Comment: Extended simulation study. Added an application to a new data se
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling
This paper considers learning a product graph from multi-attribute graph
signals. Our work is motivated by the widespread presence of multilayer
networks that feature interactions within and across graph layers. Focusing on
a product graph setting with homogeneous layers, we propose a bivariate
polynomial graph filter model. We then consider the topology inference problems
thru adapting existing spectral methods. We propose two solutions for the
required spectral estimation step: a simplified solution via unfolding the
multi-attribute data into matrices, and an exact solution via nearest Kronecker
product decomposition (NKD). Interestingly, we show that strong inter-layer
coupling can degrade the performance of the unfolding solution while the NKD
solution is robust to inter-layer coupling effects. Numerical experiments show
efficacy of our methods.Comment: 6 pages, 4 figures, submitted to ICASSP 202
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
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