Abstract：Missing data problems are commonly attributed to the matrix completion problem, and matrix completion is an important method of signal acquisitions following compressing sensing. The data examples have the property of multilinearity in applications, that is, the data set can be represented by higher order tensors. The tensor completion problem and its applications in face recognition are studied. Based on lowerdimensional Tucker decomposition of tensors, an iterative algorithm is proposed to complete tensors. And the distance between the estimating tensor and its Tucker approximation tensor is monotonically decreasing during the iterative procedure. Experimental results demonstrate the effectiveness and feasibility of the proposed method in completing tensor and face recognition.
2011, Vol. 24 
