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Multi-view subspace learning (MSL) aims to find a low-dimensional subspace ofthe data obtained from multiple views. Different from single view case, MSLshould take both common and specific knowledge among different views intoconsideration. To enhance the robustness of model, the complexity,non-consistency and similarity of noise in multi-view data should be fullytaken into consideration. Most current MSL methods only assume a simpleGaussian or Laplacian distribution for the noise while neglect the complexnoise configurations in each view and noise correlations among different viewsof practical data. To this issue, this work initiates a MSL method by encodingthe multi-view-shared and single-view-specific noise knowledge in data.Specifically, we model data noise in each view as a separated Mixture ofGaussians (MoG), which can fit a wider range of complex noise types thanconventional Gaussian/Laplacian. Furthermore, we link all single-view-noise asa whole by regularizing them by a common MoG component, encoding the sharednoise knowledge among them. Such regularization component can be formulated asa concise KL-divergence regularization term under a MAP framework, leading togood interpretation of our model and simple EM-based solving strategy to theproblem. Experimental results substantiate the superiority of our method.

Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians