L1-regularized boltzmann machine learning using majorizer minimization

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5 Citations (Scopus)


We propose an inference method to estimate sparse interactions and biases according to Boltzmann machine learning. The basis of this method is L1 regularization, which is often used in compressed sensing, a technique for reconstructing sparse input signals from undersampled outputs. L1 regularization impedes the simple application of the gradient method, which optimizes the cost function that leads to accurate estimations, owing to the cost function's lack of smoothness. In this study, we utilize the majorizer minimization method, which is a well-known technique implemented in optimization problems, to avoid the non-smoothness of the cost function. By using the majorizer minimization method, we elucidate essentially relevant biases and interactions from given data with seemingly strongly-correlated components.

Original languageEnglish
Article number054801
Journaljournal of the physical society of japan
Issue number5
Publication statusPublished - 2015 May 15
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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