Dictionary Learning for Incoherent Sampling
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Nov 14, 2013
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May 14, 2012
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May 14, 2012
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Machine learning techniques are used to train a “dictionary” of input signal elements, such that input signals can be linearly decomposed into a few, sparse elements. This prior knowledge on the sparsity of the input signal leads to excellent reconstruction results via maximum-aposteriori estimation. The machine learning imposes certain properties on the learned dictionary (specifically, low coherence with the system response), which properties are important for reliable reconstruction.
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