Uncorrelated slow feature discriminant analysis using globality preserving projections for feature extraction

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method for classification inspired by biological mechanism. However, SFDA only considers the local geometrical structure information of data and ignores the global geometrical structure information. Furthermore, previous works have demonstrated that uncorrelated features of minimum redundancy are effective for classification. In this paper, a novel method called uncorrelated slow feature discriminant analysis using globality preserving projections (USFDA-GP) is proposed for feature extraction and recognition. In USFDA-GP, two kinds of global information are imposed to the objective function of conventional SFDA for respecting some more global geometric structures. We also provide an analytical solution by simple eigenvalue decomposition to the optimal model instead of previous iterative method. Experimental results on Extended YaleB, CMU PIE and LFW-a face databases demonstrate the effectiveness of our proposed method.