Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and
clean data, has been widely exploited to learn statistically consistent classifiers. The …

Partial label learning (PLL), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

Multi-label classification aims to learn classification models from instances associated with
multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels …

Multilabel data contains rich label semantic information, and its data structure conforms to
the cognitive laws of the actual world. However, these data usually involve many irrelevant …

Multi-label classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …

Label noise in real-world datasets encodes wrong correlation patterns and impairs the
generalization of deep neural networks (DNNs). It is critical to find efficient ways to detect …

Partial label learning aims to induce a multi-class classifier from training examples where
each of them is associated with a set of candidate labels, among which only one is the …