Co-embedding of structurally missing data by locally linear alignment

Authors:
Takehisa Yairi
Affiliations:
Research Center for Advanced Science and Technology, University of Tokyo, Japan
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2012

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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).