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Poster
Convex Two-Layer Modeling with Latent Structure
Vignesh Ganapathiraman · Xinhua Zhang · Yaoliang Yu · Junfeng Wen

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #47
in Tuesday Posters »

Unsupervised learning of structured predictors has been a long standing pursuit in machine learning. Recently a conditional random field auto-encoder has been proposed in a two-layer setting, allowing latent structured representation to be automatically inferred. Aside from being nonconvex, it also requires the demanding inference of normalization. In this paper, we develop a convex relaxation of two-layer conditional model which captures latent structure and estimates model parameters, jointly and optimally. We further expand its applicability by resorting to a weaker form of inference---maximum a-posteriori. The flexibility of the model is demonstrated on two structures based on total unimodularity---graph matching and linear chain. Experimental results confirm the promise of the method.

Keywords: (Other) Classification · Structured Prediction

Author Information

Vignesh Ganapathiraman (University Of Illinois at Chicago)
Xinhua Zhang (UIC)
Yaoliang Yu (Carnegie Mellon University)
Junfeng Wen (UofA)

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