Maximum Covariance Unfolding : Manifold Learning for Bimodal Data

NeurIPS 2011 Vijay MahadevanChi W. WongJose C. PereiraTom LiuNuno VasconcelosLawrence K. Saul

We propose maximum covariance unfolding (MCU), a manifold learning algorithm for simultaneous dimensionality reduction of data from different input modalities. Given high dimensional inputs from two different but naturally aligned sources, MCU computes a common low dimensional embedding that maximizes the cross-modal (inter-source) correlations while preserving the local (intra-source) distances... (read more)

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