Uncorrelated Lasso

In this paper, motivated by the previous sparse learning In many regression applications, there are too many unrelated based research, we propose to add variable correlation into predictors which may hide the relationship between the sparse-learning-based variable selection approach. We response and the most related predictors. A common way to note that in previous Lasso-type variable selection, variable resolve this problem is variable selection, that is to select a correlations are not taken into account, while in most subset of the most representative or discriminative predictors real-life data, predictors are often correlated. Strongly correlated from the input predictor set. The central requirement is that predictors share similar properties, and have some good predictor set contains predictors that are highly correlated overlapped information.