Feature extraction using orthogonal discriminant local tangent space alignment

Ying-Ke Lei, Yangming Xu, Jun-An Yang, Zhi-Guo Ding, Jie Gui

Abstract: A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.