F $F$-norm two-dimensional linear discriminant analysis and its application on face recognition

Two-dimensional linear discriminant analysis (2DLDA) is a widely applied extension of LDA that can cope with matrix input samples directly. However, its construction is based on a squared F $F$-norm which will lead to sensitivity to noise and outliers. In this paper, a square-free F $F$-norm 2DLDA is proposed to improve the robustness of 2DLDA. By losing the squared operation, the proposed method weakens the influence of outliers and noise and at the same time keeps the geometric structure of data. It can be solved through an effective nongreedy iterative algorithm, with each subproblem having a closed-form solution. The algorithm is further proved to be convergent. Experiments on several human face image databases demonstrate the effectiveness and robustness of the proposed method.

Automated summary

2DLDA is an extension of LDA that can handle matrix input samples directly. However, it is sensitive to noise and outliers. In this paper, a square-free F-norm 2DLDA is proposed to improve its robustness. By eliminating the squared operation, the proposed method weakens the influence of outliers and noise while preserving the geometric structure of data.