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The background model is formulated as a linear and sparse combination of atoms in a pre-learned dictionary.
SRC and variants (e.g. RSC) seek for sparse combination of bases, which means choosing a set of coefficients with very few elements greater than zero.
Here, the co-occurrence matrix construction is based on SC and we assumed each observation is a sparse combination of the lexicon elements.
Mei et al. [15] first propose the L1 tracker by casting the tracking problem as finding a sparse combination of a target template set and a trivial template set to approximate the target object.
Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM -type analySPM -typed banalysisa sparse graph methodwhere temporal dynamics at each voxel position are describaseds a sparse conbination of global brain dynamicsparse
To implement this approach we would need to select an MKL method that typically gives a sparse combination of kernel coefficients.
Similar(52)
Sparse representation [ 38] models noiseless signal as a sparse linear combination of basis elements, termed atoms, from a dictionary.
Sparse representation (SR) is a principle that a signal can be approximated by a sparse linear combination of dictionary atoms [ 14].
Sparse representation (SR) [ 5] is a parsimonious principle that a sample can be approximated by a sparse linear combination of basis vectors.
As such, each individual model is represented as a sparse linear combination of the basis vectors.
It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com