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A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
Abstract : In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method. Our algorithm sweeps the training set by randomly selecting a mini-batch of data at each iteration, and it allows us to update the variables in a block coordinate manner. Our approach leverages the proximity operator of the logistic loss, which is expressed with the generalized Lambert W function. Experiments carried out on standard datasets demonstrate the efficiency of our approach w.r.t. stochastic gradient-like methods.
Cited literature [45 references]
https://hal.archives-ouvertes.fr/hal-01672507
Contributor : Giovanni Chierchia Connect in order to contact the contributor
Submitted on : Monday, December 25, 2017 - 9:50:25 PM
Last modification on : Thursday, May 12, 2022 - 8:38:02 AM
Contributor : Giovanni Chierchia Connect in order to contact the contributor
Submitted on : Monday, December 25, 2017 - 9:50:25 PM
Last modification on : Thursday, May 12, 2022 - 8:38:02 AM
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