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Spectral Latent Variable Models for Perceptual Inference
2007
2007 IEEE 11th International Conference on Computer Vision
We propose non-linear generative models referred to as Sparse Spectral Latent Variable Models (SLVM), that combine the advantages of spectral embeddings with the ones of parametric latent variable models: (1) provide stable latent spaces that preserve global or local geometric properties of the modeled data; (2) offer low-dimensional generative models with probabilistic, bi-directional mappings between latent and ambient spaces, (3) are probabilistically consistent (i.e., reflect the data
doi:10.1109/iccv.2007.4408845
dblp:conf/iccv/KanaujiaSM07
fatcat:alca33lpzfegrdzgaz52hln5bm