Hanif, Muhammad; Seghouane, Abd-Krim
Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representations problems. These algorithms consist of two stages: the sparse coding stage and dictionary update stage. This latter stage can be achieved sequentially or in parallel. In this work, the maximum likelihood approach is used to derive a new approach to dictionary learning. The proposed method differs from recent dictionary learning algorithms for sparse representation by...[Show more]
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