Structured channel pruning for convolutional neural networks (CNNs) plays an important role in pattern recognition and deep learning. Nevertheless, existing methods usually calculate channel importance scores based on the magnitudes of individual filters, making it difficult to distinguish shared redundancy from individual channel contributions. To tackle this limitation, we propose a novel structured channel pruning method termed bi-sparse matrix factorization (BSMF). Specifically, BSMF characterizes the convolutional weight matrix as output-channel coefficients over a shared basis with row-wise $\ell_{2,p}$-norm and column-wise $\ell_{2,q}$-norm regularization terms, respectively. Note that $p,q\in[0,1)$, which, to our best knowledge, has not been explored in the literature. To solve the resulting non-convex optimization problem, we develop an efficient update scheme built upon the alternating direction method of multipliers (ADMM), in which all subproblems admit closed-form solutions. After that, a compact CNN is obtained by reconstructing the convolutional filters from the learned factors and discarding the redundant channels. Extensive experiments on the CIFAR-10 and ImageNet-1K datasets demonstrate that our proposed BSMF achieves significant network compression while maintaining high reconstruction accuracy. The source code is publicly available at https://github.com/xianchaoxiu/BSMF

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