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ICML
2010
IEEE
13 years 5 months ago
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 fac...
Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, His...
PAKDD
2009
ACM
124views Data Mining» more  PAKDD 2009»
13 years 11 months ago
Dynamic Exponential Family Matrix Factorization
Abstract. We propose a new approach to modeling time-varying relational data such as e-mail transactions based on a dynamic extension of matrix factorization. To estimate effectiv...
Kohei Hayashi, Junichiro Hirayama, Shin Ishii
CVPR
2007
IEEE
14 years 6 months ago
Modeling Appearances with Low-Rank SVM
Several authors have noticed that the common representation of images as vectors is sub-optimal. The process of vectorization eliminates spatial relations between some of the near...
Lior Wolf, Hueihan Jhuang, Tamir Hazan