ℓ1-minimization method for link flow correction

Yin, Pengbin; Sun, Zhe; Jin, Wen-Long; Xin, Jack

doi:10.1016/j.trb.2017.08.006

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…On the other hand, the null space property is a necessary and sufficient condition for ℓ 1 minimization to guarantee exact recovery of sparse solutions [16,23]. Being able to yield sparse solutions, the ℓ 1 norm has gained popularity in other types of inverse problems such as compressed imaging [33,57] and image segmentation [34,35,42] and in various fields of applications such as geoscience [74], medical imaging [33,57], machine learning [10,36,67,78,89], and traffic flow network [91]. Unfortunately, element-wise sparsity by ℓ 1 or ℓ 2 regularization in CNNs may not yield meaningful speedup as the number of filters and channels required for computation and inference may remain the same [86].…”

Section: Preliminariesmentioning

confidence: 99%

Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization

Bui

Park

Zhang

et al. 2021

Front. Appl. Math. Stat.

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Convolutional neural networks (CNN) have been hugely successful recently with superior accuracy and performance in various imaging applications, such as classification, object detection, and segmentation. However, a highly accurate CNN model requires millions of parameters to be trained and utilized. Even to increase its performance slightly would require significantly more parameters due to adding more layers and/or increasing the number of filters per layer. Apparently, many of these weight parameters turn out to be redundant and extraneous, so the original, dense model can be replaced by its compressed version attained by imposing inter- and intra-group sparsity onto the layer weights during training. In this paper, we propose a nonconvex family of sparse group lasso that blends nonconvex regularization (e.g., transformed ℓ1, ℓ1−ℓ2, and ℓ0) that induces sparsity onto the individual weights and ℓ2,1 regularization onto the output channels of a layer. We apply variable splitting onto the proposed regularization to develop an algorithm that consists of two steps per iteration: gradient descent and thresholding. Numerical experiments are demonstrated on various CNN architectures showcasing the effectiveness of the nonconvex family of sparse group lasso in network sparsification and test accuracy on par with the current state of the art.

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