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• W117482296 abstract "We introduce a novel framework, called sparse embedding (SE), for simultaneous dimensionality reduction and dictionary learning. We formulate an optimization problem for learning a transformation from the original signal domain to a lower-dimensional one in a way that preserves the sparse structure of data. We propose an efficient optimization algorithm and present its non-linear extension based on the kernel methods. One of the key features of our method is that it is computationally efficient as the learning is done in the lower-dimensional space and it discards the irrelevant part of the signal that derails the dictionary learning process. Various experiments show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive algorithms on signal recovery and object classification tasks." @default.
• W117482296 created "2016-06-24" @default.
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• W117482296 date "2012-01-01" @default.
• W117482296 modified "2023-09-30" @default.
• W117482296 title "Sparse Embedding: A Framework for Sparsity Promoting Dimensionality Reduction" @default.
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• W117482296 doi "https://doi.org/10.1007/978-3-642-33783-3_30" @default.
• W117482296 hasPublicationYear "2012" @default.
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