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Co-embedding of Structurally Missing Data by Locally Linear Alignment

  • Takehisa Yairi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

This paper proposes a “co-embedding” method to embed the row and column vectors of an observation matrix data whose large portion is structurally missing into low-dimensional latent spaces simultaneously. A remarkable characteristic of this method is that the co-embedding is efficiently obtained via eigendecomposition of a matrix, unlike the conventional methods which require iterative estimation of missing values and suffer from local optima. Besides, we extend the unsupervised co-embedding method to a semi-supervised version, which is reduced to a system of linear equations.In an experimental study, we apply the proposed method to two kinds of tasks – (1) Structure from Motion (SFM) and (2) Simultaneous Localization and Mapping (SLAM).

Keywords

Dimensionality Reduction Singular Value Decomposition Latent Vector Wireless Device Latent Semantic Indexing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Takehisa Yairi
    • 1
  1. 1.Research Center for Advanced Science and TechnologyUniversity of TokyoJapan

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