AI/ML

Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression.


Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression.

Neural Netw. 2020 Sep 02;132:220-231

Authors: Le Thi HA, Le HM, Phan DN, Tran B

Abstract
We consider the large sum of DC (Difference of Convex) functions minimization problem which appear in several different areas, especially in stochastic optimization and machine learning. Two DCA (DC Algorithm) based algorithms are proposed: stochastic DCA and inexact stochastic DCA. We prove that the convergence of both algorithms to a critical point is guaranteed with probability one. Furthermore, we develop our stochastic DCA for solving an important problem in multi-task learning, namely group variables selection in multi class logistic regression. The corresponding stochastic DCA is very inexpensive, all computations are explicit. Numerical experiments on several benchmark datasets and synthetic datasets illustrate the efficiency of our algorithms and their superiority over existing methods, with respect to classification accuracy, sparsity of solution as well as running time.

PMID: 32919312 [PubMed – as supplied by publisher]

Source link

Related posts

IoT solutions are enabling physical distancing

Newsemia

What would make you trust an AI assistant? How about a face?

Newsemia

Breaking: CMS Proposes Historic Changes to Restore the Doctor-Patient Relationship & Streamline Clinical Billing

Newsemia

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy

COVID-19

COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World