Multilinear isometric embedding for visual pattern analysis

Yan Liu, Yang Liu, Chun Chung Chan

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

9 Citations (Scopus)

Abstract

This paper proposes a novel tensor based dimensionality reduction algorithm called Multilinear Isometric Embedding (MIE) based on a representative manifold learning algorithm Isomap. Unlike Isomap that unfolds input data to the vector form, MIE directly works on more general tensor representation and utilizes iterative strategy to seek the low-dimensional equivalence, which best preserves the global geometry. By avoiding the problems caused by data vectorization, MIE reduces the data analysis difficulty and computational cost. More importantly, MIE keeps the intrinsic tensor structure of the data in low-dimensional representation. Meanwhile, MIE inherits the merits of Isomap, i.e., the ability of uncovering the global geometry of high-dimensional observations. By providing explicit embedding function, MIE makes the embedding of new data points to the low-dimensional space straightforward. Experiments on various datasets validate the effectiveness of proposed method.
Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages212-218
Number of pages7
DOIs
Publication statusPublished - 1 Dec 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: 27 Sep 20094 Oct 2009

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
CountryJapan
CityKyoto
Period27/09/094/10/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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