450 citations · 1.2k across the 59 of their papers we have counts for
5 papers · 2 filters
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…