Alternating binary classifier and graph learning from partial labels

Cheng Yang, Gene Cheung, Vladimir Stankovic

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)
25 Downloads (Pure)

Abstract

Semi-supervised binary classifier learning is a fundamental machine learning task where only partial binary labels are observed, and labels of the remaining data need to be interpolated. Leveraging on the advances of graph signal processing (GSP), recently binary classifier learning is posed as a signal restoration problem regularized using a graph smoothness prior, where the undirected graph consists of a set of vertices and a set of weighted edges connecting vertices with similar features. In this paper, we improve the performance of such a graph-based classifier by simultaneously optimizing the feature weights used in the construction of the similarity graph. Specifically, we start by interpolating missing labels by first formulating a boolean quadratic program with a graph signal smoothness objective, then relax it to a convex semi-definite program, solvable in polynomial time. Next, we optimize the feature weights used for construction of the similarity graph by reusing the smoothness objective but with a convex set constraint for the weight vector. The reposed convex but non-differentiable problem is solved via an iterative proximal gradient descent algorithm. The two steps are solved alternately until convergence. Experimental results show that our alternating classifier / graph learning algorithm outperforms existing graph-based methods and support vector machines with various kernels.
Original languageEnglish
Number of pages4
DOIs
Publication statusPublished - 12 Nov 2018
EventAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018
https://www.apsipa2018.org/default.asp

Conference

ConferenceAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018
Abbreviated titleAPSIPA ASC 2018
CountryUnited States
CityHonolulu
Period12/11/1815/11/18
Internet address

Keywords

  • binary classifier learning
  • graph signal processing (GSP)
  • a graph-based classifier

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