Tree Search Network for Sparse Regression
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We consider the classical sparse regression problem of recovering a sparse signal x_0 given a measurement vector y = Φ x_0+w. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sparse regression by performing a tree search with pruning. It is observed in both noiseless and noisy cases, TSN recovers synthetic and real signals with lower complexity than a conventional tree search and is superior to existing algorithms by a large margin for various types of the sensing matrix Φ, widely used in sparse regression.
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