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Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.098
Abstract: Abstract Data often exists in subspaces embedded within a high-dimensional space. Subspace clustering seeks to group data according to the dimensions relevant to each subspace. This requires the estimation of subspaces as well as the…
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Keywords:
hub based;
high dimensional;
subspace clustering;
based subspace ... See more keywords
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Published in 2019 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2018.2879681
Abstract: Most applications of radar imagery require processing techniques which achieve one fundamental goal: characterize and detect the constituent scatterers for each pixel in the scene. In this paper, we take a new look at the…
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Keywords:
subspace projections;
scattering mechanisms;
using polarimetric;
subspace ... See more keywords
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Published in 2025 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2025.3528470
Abstract: The self-expressive strategy has shown excellent capabilities in realizing low-dimensional representations of high-dimensional data for subspace clustering algorithms. The existing designs, however, are formulated on the linearization assumptions of the data, neglecting the precise characterization…
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Keywords:
subspace clustering;
self expressive;
based subspace;
metric learning ... See more keywords
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Published in 2022 at "Journal of X-ray science and technology"
DOI: 10.3233/xst-221262
Abstract: PURPOSE Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain…
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Keywords:
projection;
subspace identification;
based subspace;
low dose ... See more keywords