As a main branch of weakly supervised learning paradigm, partial label learning (PLL)
copes with the situation where each sample corresponds to ambiguous candidate labels …

For classification with few labeled and massive unlabeled patterns, co-training, which uses
information in labeled and unlabeled data to classify query patterns, is often employed to …

In deep multi-view clustering, three intractable problems are posed ahead of researchers,
namely, the complementarity exploration problem, the information preservation problem …

Label distribution learning (LDL) has garnered increased attention in recent studies on label
ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming …

Partial label learning (PLL) studies the problem of learning instance classification with a set
of candidate labels and only one is correct. While recent works have demonstrated that the …

Convolutional neural network (CNN) has been successfully employed for synthetic aperture
radar automatic target recognition (SAR-ATR). Whereas, few labeled synthetic aperture …

Image ordinal estimation is to estimate the ordinal label of a given image. Existing methods
primarily rely on ordinal regression, mapping feature representations directly to ordinal …

Graph representation learning aims to learn low-dimensional representation for the graph,
which has played a vital role in real-world applications. Without requiring additional labeled …

In this work, we study the multi-view partial label learning (MVPLL) problem, where each
instance is depicted by different view features and associated with a set of candidate labels …

Partial label learning (PLL) is a weakly supervised methodology dealing with tasks that have
annotation problems by replacing the single label with a collection of candidate labels …