Metric Learning with Rank and Sparsity Constraints
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Approximating a signal or an image with a sparse linear expansion from an over-complete dictionary of atoms is an extremely useful tool to solve many signal processing problems. Finding the sparsest approximation of a signal from an arbitrary dictionary is ...
In many applications - such as compression, de-noising and source separation - a good and efficient signal representation is characterized by sparsity. This means that many coefficients are close to zero, while only few ones have a non-negligible amplitude ...
This correspondence deals with multiwavelets, which are a recent generalization of wavelets in the context of time-varying filter banks and with their applications to signal processing and especially com- pression. By their inherent structure, multiwavelet ...
Approximating a signal or an image with a sparse linear expansion from an overcomplete dictionary of atoms is an extremely useful tool to solve many signal processing problems. Finding the sparsest approximation of a signal from an arbitrary dictionary is ...
This paper deals with multiwavelets which are a recent generalization of wavelets in the context of multirate filter banks and with their applications to signal processing and especially compression. By their inherent structure, multiwavelets are fit for p ...
