19,270 research outputs found
Fusing Data with Correlations
Many applications rely on Web data and extraction systems to accomplish
knowledge-driven tasks. Web information is not curated, so many sources provide
inaccurate, or conflicting information. Moreover, extraction systems introduce
additional noise to the data. We wish to automatically distinguish correct data
and erroneous data for creating a cleaner set of integrated data. Previous work
has shown that a na\"ive voting strategy that trusts data provided by the
majority or at least a certain number of sources may not work well in the
presence of copying between the sources. However, correlation between sources
can be much broader than copying: sources may provide data from complementary
domains (\emph{negative correlation}), extractors may focus on different types
of information (\emph{negative correlation}), and extractors may apply common
rules in extraction (\emph{positive correlation, without copying}). In this
paper we present novel techniques modeling correlations between sources and
applying it in truth finding.Comment: Sigmod'201
Minimum information loss fusion in distributed sensor networks
A key assumption of distributed data fusion is
that individual nodes have no knowledge of the global network
topology and use only information which is available locally.
This paper considers the weighted exponential product (WEP)
rule as a methodology for conservatively fusing estimates with
an unknown degree of correlation between them. We provide a
preliminary investigation into how the methodology for selecting
the mixing parameter can be used to minimize the information
loss in the fused covariance as opposed to reducing the Shannon
entropy, and hence maximize the information of the fused
covariance. Our results suggest that selecting a mixing parameter
which minimizes the information loss ensures that information
which is exclusive to the estimates from one source is not lost
during the fusion process. These results indicate that minimizing
the information loss provides a robust technique for selecting the
mixing parameter in WEP fusion
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i.e. multi-modal) has been
increasingly applied in the field of biomedical imaging. One of the challenges
in performing the multimodal analysis is that there exist multiple schemes for
fusing the information from different modalities, where such schemes are
application-dependent and lack a unified framework to guide their designs. In
this work we firstly propose a conceptual architecture for the image fusion
schemes in supervised biomedical image analysis: fusing at the feature level,
fusing at the classifier level, and fusing at the decision-making level.
Further, motivated by the recent success in applying deep learning for natural
image analysis, we implement the three image fusion schemes above based on the
Convolutional Neural Network (CNN) with varied structures, and combined into a
single framework. The proposed image segmentation framework is capable of
analyzing the multi-modality images using different fusing schemes
simultaneously. The framework is applied to detect the presence of soft tissue
sarcoma from the combination of Magnetic Resonance Imaging (MRI), Computed
Tomography (CT) and Positron Emission Tomography (PET) images. It is found from
the results that while all the fusion schemes outperform the single-modality
schemes, fusing at the feature level can generally achieve the best performance
in terms of both accuracy and computational cost, but also suffers from the
decreased robustness in the presence of large errors in any image modalities.Comment: Zhe Guo and Xiang Li contribute equally to this wor
Low-Dimensional Topology of Information Fusion
We provide an axiomatic characterization of information fusion, on the basis
of which we define an information fusion network. Our construction is
reminiscent of tangle diagrams in low dimensional topology. Information fusion
networks come equipped with a natural notion of equivalence. Equivalent
networks `contain the same information', but differ locally. When fusing
streams of information, an information fusion network may adaptively optimize
itself inside its equivalence class. This provides a fault tolerance mechanism
for such networks.Comment: 8 pages. Conference proceedings version. Will be superceded by a
journal versio
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