  There has been a great effort to transfer linear discriminant techniques that
operate on vector data to high-order data, generally referred to as Multilinear
Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing
the inter-class variances to intra-class variances defined on tensor data
representations. However, there has not been any attempt to employ
class-specific discrimination criteria for the tensor data. In this paper, we
propose a multilinear subspace learning technique suitable for applications
requiring class-specific tensor models. The method maximizes the discrimination
of each individual class in the feature space while retains the spatial
structure of the input. We evaluate the efficiency of the proposed method on
two problems, i.e. facial image analysis and stock price prediction based on
limit order book data.
