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cs.LG2026
Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery
Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1
The convex Latent Optimal Partition (LOP)-l2/l1 approach enables block-sparse signal recovery with unknown partitions but relies on manual hyperparameter tuning. Additionally, nume…
cs.LG2026
Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties
Takanobu Furuhashi, Hiroki Kuroda, Masahiro Yukawa +3
We propose two nonconvex regularization methods, LogLOP-l2/l1 and AdaLOP-l2/l1, for recovering block-sparse signals with unknown block partitions. These methods address the underes…
cs.LG2025
WEEP: A Differentiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope
Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1
Sparse regularization is fundamental in signal processing and feature extraction but often relies on non-differentiable penalties, conflicting with gradient-based optimizers. We pr…