Sparse distance metric learning

Tze Choy and Nicolai Meinshausen ()

Computational Statistics, 2014, vol. 29, issue 3, 515-528

Abstract: Nearest neighbour classification requires a good distance metric. Previous approaches try to learn a quadratic distance metric learning so that observations of different classes are well separated. For high-dimensional problems, where many uninformative variables are present, it is attractive to select a sparse distance metric, both to increase predictive accuracy but also to aid interpretation of the result. We investigate the $$\ell 1$$ ℓ 1 -regularized metric learning problem, making a connection with the Lasso algorithm in the linear least squared settings. We show that the fitted transformation matrix is close to the desired transformation matrix in $$\ell 1$$ ℓ 1 -norm by assuming a version of the compatibility condition. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Sparse recovery; Multiclass; Lasso; High-dimensional; Consistency (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:29:y:2014:i:3:p:515-528

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DOI: 10.1007/s00180-013-0437-2

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