Multiway data analysis
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge and Information Systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
Supervised manifold learning for image and video classification
Proceedings of the international conference on Multimedia
Expert Systems with Applications: An International Journal
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This paper presents a novel dimensionality reduction technique named Tensor Distance based Multilinear Multidimensional Scaling (TD-MMDS). First, we propose a new distance metric called Tensor Distance (TD) to build a relationship graph of data points with high-order. Then we employ an iterative strategy to sequentially learn the transformation matrices that can best keep pair-wise TDs of the high-order data in the low-dimensional embedded space. By integrating both tensor distance and tensor embedding, TD-MMDS provides a uniform framework of tensor based dimensionality reduction, which preserves the intrinsic structure of high-order data through the whole learning procedure. Experiments on standard image and video datasets validate the effectiveness of the proposed TD-MMDS.