Semi-supervised visual recognition with constrained graph regularized non negative matrix factorization

This paper proposes a semi-supervised nonnegative matrix factorization algorithm for face and gait recognition. The proposed algorithm imposes hard constraints on the labelled data points, such that the data points that belong to the same class are projected to the same lower dimensional point. In addition, it introduces a graph Laplacian regularization term that preserves the local geometry structure of the data by penalising large distances between the projections of points that are close in the original space. This results in a constrained optimization problem, that is solved using block coordinate descent with multiplicative update rules. Experimental results on several publicly available datasets demonstrate that proposed method performs in par or considerably better than state of the art methods.