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The paper presented a systematic evaluation of the weight sparsity regularization schemes for the deep neural networks applied to the whole brain resting-state functional magnetic resonance imaging data. The weight sparsity regularization was deployed between the visible and hidden layers of the Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM), in which the L0-norm based non-zero value ratio...
Recently, an explicit control of weight sparsity level between the layers in the deep neural network has been proposed and gainfully been utilized to resting-state fMRI (rfMRI) data. However, the reliability of the weight sparsity control scheme via the percentage of non-zero weights (PNZ) was not systematically evaluated in term of the convergence property of the sparsity levels across various scenarios...
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