Although of major importance for research and breeding, sequence resources for rye were sparse imposing serious limitations for trait mapping, association studies, and functional genomics in rye.
To alleviate these limitations, sparse-coding-based SPM (ScSPM) [12], local coordinate coding (LCC) [20], and locality-constrained linear coding (LLC) [13] aim at obtaining a nonlinear feature representation which works better with linear classifiers.
Unfortunately, such an assumption no longer holds in anisotropic networks which can be commonly found, and this assumption can be invalidated by a variety of factors: obstacles, resource limitation, sparse and non-uniform node distribution, irregular radio propagation pattern, and irregular regions [14].
In systems with resource constraints, such as actuation limitations in sparse control applications or limited bandwidth in networked control systems, it is desirable to use control signals that are either sparse or sporadically changing in time.
As a result of these two limitations, namely sparse sampling and approximation of meanings with words, individual word coordinates are subject to considerable noise, the relative amplitude of which can be roughly estimated as 10 20%.
Almost all evidence pertaining to the effect of demographic risk factors was available only for SSD, and limitations included sparse replication and diverse categorisation of variables across studies.
The third approach overcame some of these limitations of sparse data by using a Bayesian implementation of the hidden Markov model (26).
Significance of the genotype test was obtained with the Markov Chain Monte Carlo method to avoid the limitations that sparse contingency tables pose to the asymptotic chi squared distribution method.
We, however, expect that regardless of the choice of imputation method this remained a sparse dataset with substantial limitations which should be acknowledged and carefully considered when applying this model for future simulation purposes.
An obvious limitation of sparse mobile crowdsensing is that it may not be ideal for applications in which data quantity is as important as data quality.
