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Poster
Generalized Unsupervised Manifold Alignment
Zhen Cui · Hong Chang · Shiguang Shan · Xilin Chen

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D
in Poster Session »

In this paper, we propose a generalized Unsupervised Manifold Alignment (GUMA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual embedding space. The main benefits of this model include: (1) simultaneous discovery and alignment of manifold structures; (2) fully unsupervised matching without any pre-specified correspondences; (3) efficient iterative alignment without computations in all permutation cases. Experimental results on dataset matching and real-world applications demonstrate the effectiveness and the practicability of our manifold alignment method.

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Author Information

Zhen Cui (Vi­sion and Ma­chine Learn­ing Lab)
Hong Chang (Chinese Academy of Sciences)
Shiguang Shan (Chinese Academy of Sciences)
Xilin Chen (Institute of Computing Technology, Chinese Academy of Sciences)

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