We propose a new matrix completion algorithm— Kernelized Probabilistic Matrix Factorization (KPMF), which effectively incorporates external side information into the matrix fac...
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...
The sequence kernel has been shown to be a promising kernel function for learning from sequential data such as speech and DNA. However, it is not scalable to massive datasets due ...
Makoto Yamada, Masashi Sugiyama, Gordon Wichern, T...