# $\ell_P$ Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data

@article{Zhao2019ell_PNI,
title={\$\ell_P\$ Norm Independently Interpretable Regularization Based Sparse Coding for Highly Correlated Data},
author={Haoli Zhao and Shuxue Ding and Xiang Li and Lingjun Zhao},
journal={IEEE Access},
year={2019},
volume={7},
pages={53542-53554}
}
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 greatly. However, commonly used sparse regularization may not be robust in high-coherence condition. In this paper, inspired from independently interpretable lasso (IILasso), which considers the… CONTINUE READING

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