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Bilinear Sparse Coding for Invariant Vision
2005
Neural Computation
Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that
doi:10.1162/0899766052530893
pmid:15563747
fatcat:byywmfsk3bfpxh4c53tbvprfmm