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ICML
2010
IEEE
14 years 16 days 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»
14 years 6 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
15 years 1 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