Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

To uncover an appropriate latent subspace for data representation, in this paper we propose
a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

Multi-label learning deals with data belonging to different labels simultaneously. Like
traditional supervised feature selection, multi-label feature selection also plays an important …

Many pattern analysis and data mining problems have witnessed high-dimensional data
represented by a large number of features, which are often redundant and noisy. Feature …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

We consider a hypercube view to perceive the label space of multilabel classification
problems geometrically. The view allows us not only to unify many existing multilabel …