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Fast Proximal Gradient Methods with Node Pruning for Tree-Structured Sparse Regularization
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AI
DOI：10.1007/978-3-032-06096-9_7.png)
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Abstract
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Sparse learning with structural information is a fundamental framework for feature selection. Among the various structures, the tree is a basic one that appears in feature vectors, and tree-structured regularization has been utilized to incorporate trees into objective functions. Although proximal gradient methods (PGMs) are usually used for optimization, they incur high computation costs for deep tree structures or large datasets. We propose a fast PGM for tree-structured regularization. Our method safely skips parameter updates of PGMs for pruning unnecessary leaf nodes in the tree. In addition, it prunes unnecessary computations for internal nodes in a hierarchical manner. Our method guarantees the same optimization results and convergence rate as the original method. Furthermore, it can be applied to various PGMs for tree-structured regularization. Experiments show that our method reduces the processing time by up to 56% from the original method without degrading accuracy.
Keywords:
Sparse learning
Feature selection
Pruning method
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