Bidirectional visible neighborhood preserving embedding

Yang Liu, Yan Liu, Chun Chung Chan

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

Abstract

In this paper, we propose a series of dimensionality reduction algorithms according to a novel neighborhood graph construction method. This paper begins with the presentation of a new manifold learning algorithm called bidirectional visible neighborhood preserving embedding (BVNPE). Similar with existing manifold techniques, BVNPE first links every data point with its k nearest neighbors (NNs). Then, we construct a reliable neighborhood graph by checking two criteria: bidirectional linkage and visible neighborhood preserving. Third, we assign the weights to each edge in this reliable graph based on the pairwise distance between data points. Finally, we compute the low-dimensional embedding, trying to preserve the manifold structure of input dataset by mapping nearby points on the manifold to nearby points in low-dimensional space. Moreover, this paper also proposes a linear BVNPE called BVNPE/L for straightforward embedding of new data, and a multilinear BVNPE called BVNPE/M, which represents the tensor structure of image and video data better. Experiments on various datasets validate the effectiveness of proposed algorithms.
Original languageEnglish
Title of host publication1st International Conference on Internet Multimedia Computing and Service, ICIMCS 2009
Pages155-160
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2009
Event1st International Conference on Internet Multimedia Computing and Service, ICIMCS 2009 - Kunming, Yunnan, China
Duration: 23 Nov 200925 Nov 2009

Conference

Conference1st International Conference on Internet Multimedia Computing and Service, ICIMCS 2009
Country/TerritoryChina
CityKunming, Yunnan
Period23/11/0925/11/09

Keywords

  • Bidirectional visible neighborhood preserving embedding
  • Dimensionality reduction
  • Manifold learning

ASJC Scopus subject areas

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

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