The first contribution is JSPCA relaxes the orthogonal constraint to freely select the useful features.
The second contribution is JSPCA integrates feature selection into subspace learning via joint l2,1-norms.
The third contribution is JSPCA provides a simple yet effective optimization algorithm and a series of theoretical analyses.