Abstract
Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/ϵ) number of iterations to find an ϵ accurate solution. Nesterov [1] broke this barrier by proposing an excessive gap reduction technique (EGR) which converges in O(1/ϵ) iterations. However, it is restricted to Euclidean projections which consequently requires an intractable amount of computation for each iteration when applied to solve M3N. In this paper, we show that by extending EGR to Bregman projection, this faster rate of convergence can be retained, and more importantly, the updates can be performed efficiently by exploiting graphical model factorization. Further, we design a kernelized procedure which allows all computations per iteration to be performed at the same cost as the state-of-the-art approaches.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
