454 citations · 3.1k across the 117 of their papers we have counts for
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Binary Classification from Positive-Confidence Data
Takashi Ishida, Gang Niu, Masashi Sugiyama
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confide…
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically…
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen +2
Modes and ridges of the probability density function behind observed data are useful geometric features. Mode-seeking clustering assigns cluster labels by associating data samples…
Learning from Complementary Labels
Takashi Ishida, Gang Niu, Weihua Hu +1
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from comp…
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods…
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis +1
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model i…