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Eigenspaces for graphs from Spectral Features

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JournalSECOND INTERNATION CONFERENCE ON IMAGE AND GRAPHICS, PTS 1 AND 2
DatePublished - 2002
Volume4875
Number of pages8
Pages (from-to)772-779
Original languageEnglish

Abstract

In this paper we explore how to embed symbolic relational graphs with unweighted edges in eigenspaces. We adopt a graph-spectral approach. The leading eigenvectors of the graph adjacency matrix are used to define clusters of nodes. For each cluster, we compute vectors of spectral properties. We embed these vectors in a pattern-space using principal components analysis and multidimensional scaling techniques. We demonstrate both methods result in well-structured view spaces for graph-data extracted from 2D views of 3D objects.

    Research areas

  • RELAXATION

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