A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well.
In this study, we present a new hyper-scale terrain analysis approach, referred to as Contextual Statistical Mapping (ConStat), which is based on statistical neighborhood measures derived for growing sparse circular neighborhoods.
Thus, a sparse model can be derived for the received signal, and accordingly sparse signal recovery algorithms can be used for estimating the target parameters including range, Doppler frequency, and angle [14 18].
We are not advocating the use of extrapolated AGB maps derived from sparse field measurements for carbon accounting: we firmly believe that AGB is best mapped using a combination of RS data calibrated and validated using a substantial number of carefully established field plots.
In the previous section, it was initially assumed that X c =X k =X k+N/2 for odd k positions, since they were derived from sparse input data.
This approach is derived from sparse based representation methods.
In addition, an extension of projection matrix design is derived for the scenarios where the signals cannot be represented exactly sparse in a specified dictionary.
CDKN1C/GAPDH ratios were derived for each run.
This can be guaranteed for the majority of the data symbols, since they are derived by sparse data.
A more general framework was further proposed to derive PNLMS adaptive algorithms for sparse system identification, which employed convex optimization [24].
