Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39827
Title: Tensor distance based multilinear multidimensional scaling for image and video analysis
Authors: Liu, Y 
Liu, Y
Keywords: Dimensionality reduction
Image and video analysis
Tensor distance
Tensor distance based multilinear multidimensional scaling
Issue Date: 2009
Source: Proceedings of 17th ACM International Conference on Multimedia, China, Oct. 19 - Oct. 23, 2009, p. 577-580 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/39827
ISBN: 978-1-60558-608-3
DOI: 10.1145/1631272.1631360
Appears in Collections:Conference Paper

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