Concurrent Subspaces Analysis

A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called Concurrent Subspaces Analysis (CSA), to derive representative subspaces by encoding image objects as 2nd or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3rd order tensor data, simulated video sequences and Gabor fil...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Dong Xu, Shuicheng Yan, Lei Zhang, HongJiang Zhang, Zhengkai Liu, Heung-Yeung Shum
• Keywords: Computer Vision, Concurrent Subspaces Analysis, CVPR 2005, Dimensionality Dilemma, Multiple Concurrent Subspaces, Order Tensor Data, Representative Subspaces