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).
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Yairi, T. (2012). Co-embedding of Structurally Missing Data by Locally Linear Alignment. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_35
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DOI: https://doi.org/10.1007/978-3-642-30220-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30219-0
Online ISBN: 978-3-642-30220-6
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