CUED Publications database

From subspace learning to distance learning: A geometrical optimization approach

Meyer, G and Journée, M and Bonnabel, S and Sepulchre, R (2009) From subspace learning to distance learning: A geometrical optimization approach. In: UNSPECIFIED pp. 385-388..

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Abstract

In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection onto that subspace. A proper weighting of the two iterations enables to continuously interpolate between the problem of learning a subspace and learning a distance when the subspace is fixed. © 2009 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Kernel and metric learning Low-rank approximation Manifold-based optimization Online learning
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:46
Last Modified: 08 Dec 2014 02:18
DOI: 10.1109/SSP.2009.5278557