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Title: Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity information is very specific, e.g., convex sparsity-inducing norms or ℓ0-norm. However, these norms cannot be directly applied to the problem of complex (non-convex) graph-structured sparsity models, which have important application in disease outbreak and social networks, etc. In this paper, we propose a stochastic gradient-based method for solving graph-structured sparsity constraint problems, not restricted to the least square loss. We prove that our algorithm enjoys a linear convergence up to a constant error, which is competitive with the counterparts in the batch learning setting. We conduct extensive experiments to show the efficiency and effectiveness of the proposed algorithms.  more » « less
Award ID(s):
1954376
NSF-PAR ID:
10145918
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 36th International Conference on Machine Learning
Volume:
97
Page Range / eLocation ID:
7563-7573
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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