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ICML
2010
IEEE

A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices

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A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact factorized representation without the need of specifying the rank beforehand. Moreover, we show that the framework can be easily generalized to the problem of learning multiple matrices and general spectral regularization. Empirically we show that we can re
Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, His
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima
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