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..Full text not available from this repository.
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|
|Divisions:||Div F > Control|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 14:04|
|Last Modified:||25 Nov 2015 10:23|