In the fine tradition of Occam's razor, the new equation has fewer variables than the old one.
However, the formulation based on a positive class of linear functions can be solved more quickly because it has fewer variables.
We showed that our model having few variables outperforms one having many variables in a generalization test.
Moreover, we have refined the mathematical model so that it has fewer variable and parameters and can better illustrate the major thesis of this study.
In the new dimensional space, each observation would have fewer variables (dimensionality reduction) and at the same time observations belonging to the same class will form lumps (clusters) and each cluster would be clearly differentiated from the other [41, 70].
Having fewer variables in their regression model may have added weight to the variables they included.
We proposed a practical SC classification method and showed that our model having fewer variables outperformed the model having many variables in the generalization test.
An optimal imputation model would have fewer variables, but including up to 1/3 would not do much harm (to continuous variables, see below).
In a whole exome sequencing study of about 3,000 Finns, Lim et al. first established that the Finns have fewer variable sites overall but more loss-of-function variants compared with non-Finnish European individuals, and subsequently identified robust associations with key traits of medical relevance.
Using extensive, rigorous numerical evaluations on a variety of test problems, we show that in contrast to the best model in the literature, our model does not decouple tasks and units, but still has fewer binary variables, constraints, and nonzeros, and is faster.
