Sparse distance metric learning

作者：Choy Tze; Meinshausen Nicolai^*

来源：Computational Statistics, 2014, 29(3-4): 515-528.

DOI：10.1007/s00180-013-0437-2

• Sparse recovery
• Multiclass
• Lasso
• High-dimensional
• Consistency

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 -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 -norm by assuming a version of the compatibility condition.

• 出版日期2014-6