Learning the geometry of common latent variables using alternating-diffusion

DOI10.1016/j.acha.2015.09.002zbMath1394.94297OpenAlexW2179941082MaRDI QIDQ1742815

Roy R. Lederman, Ronen Talmon

Publication date: 12 April 2018

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2015.09.002

zbMATH Keywords

multi-viewmultimodal analysisalternating-diffusioncommon variablediffusion-maps

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Cites Work

• Unnamed Item
• Non-linear independent component analysis with diffusion maps
• Multi-view kernel construction
• Diffusion maps
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
• 10.1162/153244303768966085
• Extensions of Lipschitz mappings into a Hilbert space
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Audio-Visual Group Recognition Using Diffusion Maps
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Joint Manifolds for Data Fusion
• Co-Transduction for Shape Retrieval
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
• RELATIONS BETWEEN TWO SETS OF VARIATES

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