Orthogonal tensor rank one differential graph preserving projections with its application to facial expression recognition

In this paper, a new tensor dimensionality reduction algorithm is proposed based on graph preserving criterion and tensor rank-one projections. In the algorithm, a novel, effective and converged orthogonalization process is given based on a differential-form objective function. A set of orthogonal rank-one basis tensors are obtained to preserve the intra-class local manifolds and enhance the inter-class margins. The algorithm is evaluated by applying to the basic facial expressions recognition.