18,898 research outputs found
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Network embedding represents nodes in a continuous vector space and preserves
structure information from the Network. Existing methods usually adopt a
"one-size-fits-all" approach when concerning multi-scale structure information,
such as first- and second-order proximity of nodes, ignoring the fact that
different scales play different roles in the embedding learning. In this paper,
we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE)
framework, which promotes the collaboration of different scales and lets them
vote for robust representations. The proposed AAANE consists of two components:
1) Attention-based autoencoder effectively capture the highly non-linear
network structure, which can de-emphasize irrelevant scales during training. 2)
An adversarial regularization guides the autoencoder learn robust
representations by matching the posterior distribution of the latent embeddings
to given prior distribution. This is the first attempt to introduce attention
mechanisms to multi-scale network embedding. Experimental results on real-world
networks show that our learned attention parameters are different for every
network and the proposed approach outperforms existing state-of-the-art
approaches for network embedding.Comment: 8 pages, 5 figure
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
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