Joint sparse principal component analysis

Shuangyan Yi, Zhihui Lai, Zhenyu He, Yiu-ming Cheung, Yang Liu

Abstract: Highlights•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.