<inline-formula> <tex-math notation="LaTeX">$\ell_P$ </tex-math></inline-formula> Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

<inline-formula> <tex-math notation="LaTeX">$\ell_P$ </tex-math></inline-formula> Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

Sparse coding, which aims at finding appropriate sparse representations of data with an overcomplete dictionary set, is a well-established signal processing methodology and has good efficiency in various areas. The varying sparse constraint can influence the performances of sparse coding algorithms...

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Bibliographic Details
Main Authors: Haoli Zhao, Shuxue Ding, Xiang Li, Lingjun Zhao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Sparse coding
highly correlated data
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weighted <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">ℓ</italic>₁ norm
independently interpretable weighted lasso
Online Access:https://ieeexplore.ieee.org/document/8691452/

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https://ieeexplore.ieee.org/document/8691452/

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