On the Landscape of Sparse Linear Networks

Abstract

Network pruning, or sparse network has a long history and practical significance in modern applications. Although the loss functions of neural networks may yield bad landscape due to non-convexity, we focus on linear activation which already owes benign landscape. With no unrealistic assumption, we conclude the following statements for the squared loss objective of general sparse linear neural networks: 1) every local minimum is a global minimum for scalar output with any sparse structure, or non-intersected sparse first layer and dense other layers with orthogonal training data; 2) sparse linear networks have sub-optimal local-min for only sparse first layer due to low rank constraint, or output larger than three dimensions due to the global minimum of a sub-network. Overall, sparsity breaks the normal structure, cutting out the decreasing path in original fully-connected networks.

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