454 citations · 3.1k across the 117 of their papers we have counts for
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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…
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…
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…