The Emerging Trends of Multi-Label Learning

Abstract

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.

Accepted to TPAMI 2021

References in corpus (14)

• Nearly unbiased variable selection under minimax concave penalty
• The sparsity and bias of the Lasso selection in high-dimensional linear regression
• A Theoretical Analysis of NDCG Type Ranking Measures
• DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
• Order-Free RNN with Visual Attention for Multi-Label Classification
• Maximum Margin Output Coding
• Noisy multi-label semi-supervised dimensionality reduction
• DECAF: Deep Extreme Classification with Label Features
• A novel online multi-label classifier for high-speed streaming data applications
• Multi-Label Learning with Deep Forest
• ECLARE: Extreme Classification with Label Graph Correlations
• Consistent Multilabel Ranking through Univariate Losses
• Large-Scale Multi-Label Learning with Incomplete Label Assignments
• Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy

Cited by in corpus (3)

• Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
• Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
• Multi-Label Learning from Single Positive Labels