Penalized partial least square discriminant analysis with ?1-norm ? 1 - norm for multi-label data
A new and effective learning method for the multi-label data is proposed. The method exploits the sparse property of the label space fully. The method captures the correlations of the variables by using partial least squares discriminant analysis. Dimension reduction has also applied to the high-dimensional data. The sparse purpose is achieved by performing the l1-norm penalty.
partial least squares discriminant analysis dimension reduction sparse learning multi-label learning