Large margin low rank tensor analysis.
We present a supervised model for tensor dimensionality reduction, which is called large margin low rank tensor analysis (LMLRTA). In contrast to traditional vector representation-based dimensionality reduction methods, LMLRTA can take any order of tensors as input. And unlike previous tensor dimensionality reduction methods, which can learn only the low-dimensional embeddings with a priori specified dimensionality, LMLRTA can automatically and jointly learn the dimensionality and the low-dimensional representations from data. Moreover, LMLRTA delivers low rank projection matrices, while it encourages data of the same class to be close and of different classes to be separated by a large margin of distance in the low-dimensional tensor space. LMLRTA can be optimized using an iterative fixed-point continuation algorithm, which is guaranteed to converge to a local optimal solution of the optimization problem. We evaluate LMLRTA on an object recognition application, where the data are represented as 2D tensors, and a face recognition application, where the data are represented as 3D tensors. Experimental results show the superiority of LMLRTA over state-of-the-art approaches.
Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, Montréal, Québec H3C 1K3, Canada guoqiang.zhong@synchromedia.ca.
Guoqiang
G
Zhong
Mohamed
M
Cheriet
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual; D021641:Recognition, Psychology