Balazs Acs, Johan Hartman, Demet Sönmez, Henrik Lindman, Anna L V Johansson, Irma Fredriksson
Real-world overall survival and characteristics of patients with ER-zero and ER-low HER2-negative breast cancer treated as triple-negative breast cancer: a Swedish population-based cohort study
The Lancet regional health. Europe
feature selection plays important role data analysis yet traditional graphbased methods often produce suboptimal results methods typically follow twostage process constructing graph datatodata affinities bipartite graph datatoanchor affinities independently selecting features based scores article largescale feature selection approach based structured bipartite graph rowsparse projection rs 2 blfs proposed overcome limitation rs 2 blfs integrates construction structured bipartite graph consisting c connected components rowsparse projection learning k nonzero rows integration allows joint selection optimal feature subset unsupervised manner notably c connected components structured bipartite graph correspond c clusters multiple subcluster centers feature makes rs 2 blfs particularly effective feature selection clustering nonspherical largescale data algorithm theoretical analysis developed solve optimization problem involved rs 2 blfs experimental results synthetic realworld datasets confirm effectiveness feature selection tasks