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Abstract
Semisupervised 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 graphbased 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 semidefinite 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 nondifferentiable 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 graphbased methods and support vector machines with various kernels.
Original language  English 

Number of pages  4 
DOIs  
Publication status  Published  12 Nov 2018 
Event  AsiaPacific Signal and Information Processing Association Annual Summit and Conference 2018  Honolulu, United States Duration: 12 Nov 2018 → 15 Nov 2018 https://www.apsipa2018.org/default.asp 
Conference
Conference  AsiaPacific Signal and Information Processing Association Annual Summit and Conference 2018 

Abbreviated title  APSIPA ASC 2018 
Country/Territory  United States 
City  Honolulu 
Period  12/11/18 → 15/11/18 
Internet address 
Keywords
 binary classifier learning
 graph signal processing (GSP)
 a graphbased classifier
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Dive into the research topics of 'Alternating binary classifier and graph learning from partial labels'. Together they form a unique fingerprint.Projects
 1 Finished

SENSIBLE: SENSors and Intelligence in BuiLt Environment (SENSIBLE) MSCA RISE
Stankovic, L., Glesk, I., Gleskova, H. & Stankovic, V.
European Commission  Horizon 2020
1/01/17 → 31/12/20
Project: Research