454 citations · 5.5k across the 247 of their papers we have counts for
7 papers · 1 filter
Information-Maximization Clustering based on Squared-Loss Mutual Information
Masashi Sugiyama, Makoto Yamada, Manabu Kimura +1
Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximi…
Relative Density-Ratio Estimation for Robust Distribution Comparison
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori +2
Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully app…
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
Gang Niu, Bo Dai, Makoto Yamada +1
We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the man…
Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise
Makoto Yamada, Masashi Sugiyama, Jun Sese
The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this pa…
Sufficient Component Analysis for Supervised Dimension Reduction
Makoto Yamada, Gang Niu, Jun Takagi +1
The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we pro…
Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization
Taiji Suzuki, Ryota Tomioka, Masashi Sugiyama
We theoretically investigate the convergence rate and support consistency (i.e., correctly identifying the subset of non-zero coefficients in the large sample limit) of multiple ke…