In this paper, we introduce a novel and robust approach to quantized matrix completion.
First, we propose a rank minimization problem with constraints induced by quantization …

Multi-label learning associates a given data instance with one or several class labels. A
frequent problem with real life multi-label datasets is the lack of complete label information …

Label distribution learning (LDL) is one of the paradigms for dealing with label ambiguity,
and it can learn the relative importance of each label to a particular instance. Most of the …

Finding a small subset of data whose linear combination spans other data points, also called
column subset selection problem (CSSP), is an important open problem in computer science …

Missing data is a prevalent problem that requires attention, as most data analysis techniques
are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where …

The problem of simultaneous column and row subset selection is addressed in this paper.
The column space and row space of a matrix are spanned by its left and right singular …

In multi-label learning, effective exploitation of the label correlations can improve
classification performance. However, due to the subjectivity of manual labeling and the …

A new paradigm for large-scale spectrum occupancy learning based on long short-term
memory (LSTM) recurrent neural networks is proposed. Studies have shown that spectrum …

In recent years, label distribution learning (LDL) has become a new learning paradigm in the
field of machine learning. LDL is mainly designed to solve the problem of ambiguity among …

Multi-task learning has attracted much attention due to growing multi-purpose research with
multiple related data sources. More-over, transduction with matrix completion is a useful …