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Shift-invariant dictionary learning (SIDL) refers to the problem of discovering a set of latent basis vectors (the dictionary) that captures informative local patterns at different locations of the input sequences, and a sparse coding for each sequence as a linear combination of the latent basis elements. It differs from conventional dictionary learning and sparse coding where the latent basis has the same dimension as the input vectors, where the focus is on global patterns instead ofdoi:10.1145/2939672.2939824 dblp:conf/kdd/ZhengYC16 fatcat:5ba3joxr7jc5fjl36kkwx5xti4