Tensor distance based multilinear multidimensional scaling for image and video analysis

Yang Liu, Yan Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages577-580
Number of pages4
DOIs
Publication statusPublished - 28 Dec 2009
Event17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China
Duration: 19 Oct 200924 Oct 2009

Conference

Conference17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
Country/TerritoryChina
CityBeijing
Period19/10/0924/10/09

Keywords

  • Dimensionality reduction
  • Image and video analysis
  • Tensor distance
  • Tensor distance based multilinear multidimensional scaling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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