This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

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 …

Due to the unavailability of labeled target data, most existing unsupervised domain
adaptation (UDA) methods alternately classify the unlabeled target samples and discover a …

Feature selection is able to select more discriminative features for classification and plays an
important role in multi-label learning to alleviate the effect of the curse of dimensionality …

Recently, image categorization has been an active research topic due to the urgent need to
retrieve and browse digital images via semantic keywords. This paper formulates image …

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 …

For the multi-label text classification problems with many classes, many existing multi-label
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …

The consistently increasing of the feature dimension brings about great time complexity and
storage burden for multi-label learning. Numerous multi-label feature selection techniques …

Increased traffic density, combined with global population development, has resulted in
increasingly congested roads, increased air pollution, and increased accidents. Globally, the …

In this paper, we propose a new deep hashing (DH) approach to learn compact binary
codes for scalable image search. Unlike most existing binary codes learning methods, which …