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A double safe screening framework for accelerating support tensor regression

delete2026-08-10
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PRE
AI
X
Xuanzhu Cui
Y
Yitian Xu *
DOI：10.1016/j.neucom.2026.134762delete
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Abstract

Abstract

En 中文
<ul class="list"> <li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content"> <div class="u-margin-s-bottom" id="p0035"> Proposes SSVI-DG, a safe screening acceleration framework for support tensor regression. </div></span></li> <li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content"> <div class="u-margin-s-bottom" id="p0040"> Derives sparsity properties of support tensor regression subproblems to support the development of safe screening rules. </div></span></li> <li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content"> <div class="u-margin-s-bottom" id="p0045"> Combines sequential screening, dynamic screening, and post-checking to ensure safe acceleration. </div></span></li> <li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content"> <div class="u-margin-s-bottom" id="p0050"> Applies SSVI-DG to both rank-one and higher rank support tensor regression models. </div></span></li> </ul>

Journal

Neurocomputing cover
Neurocomputing
IF:
6.5
Papers:
2.5W
Citations:
6.5W

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