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- W2958703003 abstract "In recent years, a statistical approach called sparse modeling has been studied extensively for estimating unobserved model parameters from a small number of observations using the sparsity of model parameters. Although sparse modeling has been applied to many practical problems in the fields of signal processing and image processing, to the best of our knowledge, few studies have applied it to the field of information networking. In this paper, we investigate whether a sparse representation of network topology can be obtained from a dictionary trained with a dictionary learning algorithm in sparse modeling. Specifically, we train a dictionary from a number of learning network topologies using the K-SVD algorithm, which is one of conventional dictionary learning algorithms, and obtain a sparse representation of the network topology by solving an l0-norm minimization problem for given network topology and the trained dictionary. Furthermore, through experiments, the effects of several factors — the network (i.e., topology and network size) and the dictionary (i.e., dictionary size) — on sparse representation of network topologies are investigated. Our finding includes that graphs whose structure is uniform (e.g., tree) and networks with cluster structure are suitable for sparse representation of network topologies." @default.
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- W2958703003 date "2019-07-01" @default.
- W2958703003 modified "2023-09-28" @default.
- W2958703003 title "Sparse Representation of Network Topology with K-SVD Algorithm" @default.
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- W2958703003 doi "https://doi.org/10.1109/compsac.2019.00050" @default.
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