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The Emerging Trends of Multi-Label Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 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 ...
Ivor Tsang, Xiaobo Shen, Haobo Wang
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Review on Multi-lable Classification [PDF]
Jisuanji kexue yu tansuo, 2023 Multi-label classification refers to the classification problem where multiple labels may coexist in a single sample. It has been widely applied in fields such as text classification, image classification, music and video classification.
LI Dongmei, YANG Yu, MENG Xianghao, ZHANG Xiaoping, SONG Chao, ZHAO Yufeng
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Fast Multi-label Learning [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications.
Xiuwen Gong +2 more
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Compact learning for multi-label classification [PDF]
Pattern Recognition, 2021 Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label compression (LC) for capturing label dependency with dimension reduction.
Jiaqi Lv +5 more
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Multi‐label learning based target detecting from multi‐frame data
IET Image Processing, 2021 In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a
Mengqing Mei, Fazhi He
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Privileged Multi-label Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performance on examples can receive the ...
Shan You +4 more
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Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
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Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
Jisuanji kexue yu tansuo, 2021 RNA-binding protein (RBP) is a total name of a class of proteins that bind to RNA (ribonucleic acid) along with the process of RNA??s regulation metabolic.
YANG Haitao, DENG Zhaohong, WANG Shitong
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Multi-label Learning with Label Enhancement [PDF]
2018 IEEE International Conference on Data Mining (ICDM), 2018 The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the instance, i.e., +1 represents relevant to the instance and -1 represents irrelevant to the instance.
Ruifeng Shao +2 more
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Multi-Label Weighted Contrastive Cross-Modal Hashing
Applied Sciences, 2023 Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and ...
Zeqian Yi +5 more
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