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Sparse and redundant representation of data assumes an ability to describe signals as linear combinations of a few atoms from a dictionary. If the model of the signal is unknown, the dictionary can be learned from a set of training signals. Like the K-SVD, many of the practical dictionary learning algorithms are composed of two main parts: sparse-coding and dictionary-update. This paper first proposes...
In many applicative scenarios it is important to derive information about the topology and the internal connections of more dynamical systems interacting together. Examples can be found in fields as diverse as economics, neuroscience and biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight...
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