Graph Regularized Constrained Non-Negative Matrix Factorization With <italic>L&#x209A;</italic> Smoothness for Image Representation

Graph Regularized Constrained Non-Negative Matrix Factorization With <italic>L&#x209A;</italic> Smoothness for Image Representation

Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years. In this paper, we propose a graph regularized constrained nonnegative matrix factorization with L<sub>p</sub> Smoothing (GCNMFS) for i...

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Bibliographic Details
Main Authors: Zhenqiu Shu, Zonghui Weng, Yunmeng Zhang, Cong-Zhe You, Zhen Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Image representation
manifold structure
graph
label information
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ</italic> smoothness
Online Access:https://ieeexplore.ieee.org/document/9143135/

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https://ieeexplore.ieee.org/document/9143135/

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