  This paper proposes a multilinear discriminant analysis network (MLDANet) for
the recognition of multidimensional objects, known as tensor objects. The
MLDANet is a variation of linear discriminant analysis network (LDANet) and
principal component analysis network (PCANet), both of which are the recently
proposed deep learning algorithms. The MLDANet consists of three parts: 1) The
encoder learned by MLDA from tensor data. 2) Features maps ob-tained from
decoder. 3) The use of binary hashing and histogram for feature pooling. A
learning algorithm for MLDANet is described. Evaluations on UCF11 database
indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA + LDA,
and MLDA in terms of classification for tensor objects.
