Scatter matrix decomposition for jointly sparse learning

Dongmei Mo, Zhihui Lai, Jie Zhou, Qinghua Hu

Abstract: Highlights•This paper solves the problem of orthogonal linear discriminant analysis (OLDA) from the novel viewpoint of scatter matrix orthogonal decomposition.•The method can obtain approximately orthogonal sparse discriminative vectors for dimensionality reduction and jointly sparse feature extraction.•Theoretical analysis shows that OLDA can be derived by the constrained scatter matrix decomposition.•The method outperforms several well-known LDA-based and sparse learning methods on four data sets (i.e., COIL100, USPS, ICADAR2003 and CMU PIE).