Robust 2DLDA based on correntropy

Abstract To further improve the robustness of two-dimensional LDA (2DLDA) methods against outliers, this paper proposes a new robust 2DLDA version which obtains the optimal projection transformation by maximizing the correntropy-based within-class similarity and maintaining the global dispersity simultaneously. The objective problem of the proposed method can be solved by an iterative optimization algorithm which is proved to converge at a local maximum point. The experimental results on FERET face database, PolyU palmprint database and Binary Alphadigits database illustrate that the proposed method outperforms three conventional 2DLDA methods when there are outliers.

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