Relevant bibliographies by topics / Multi-label embedding

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multi-label embedding'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Multi-label embedding"

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Gupta, Vivek, Rahul Wadbude, Nagarajan Natarajan, Harish Karnick, Prateek Jain, and Piyush Rai. "Distributional Semantics Meets Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3747–54. http://dx.doi.org/10.1609/aaai.v33i01.33013747.

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We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as
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Zhu, Pengfei, Qi Hu, Qinghua Hu, Changqing Zhang, and Zhizhao Feng. "Multi-view label embedding." Pattern Recognition 84 (December 2018): 126–35. http://dx.doi.org/10.1016/j.patcog.2018.07.009.

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You, Renchun, Zhiyao Guo, Lei Cui, Xiang Long, Yingze Bao, and Shilei Wen. "Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12709–16. http://dx.doi.org/10.1609/aaai.v34i07.6964.

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Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi-label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our nove
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Zhang, Jujie, Min Fang, and Huimin Chai. "Multi-label local discriminative embedding." Journal of Systems Engineering and Electronics 28, no. 5 (2017): 1009–18. http://dx.doi.org/10.21629/jsee.2017.05.19.

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Shi, Min, Yufei Tang, and Xingquan Zhu. "MLNE: Multi-Label Network Embedding." IEEE Transactions on Neural Networks and Learning Systems 31, no. 9 (2020): 3682–95. http://dx.doi.org/10.1109/tnnls.2019.2945869.

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Shi, Yaxin, Donna Xu, Yuangang Pan, Ivor W. Tsang, and Shirui Pan. "Label Embedding with Partial Heterogeneous Contexts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4926–33. http://dx.doi.org/10.1609/aaai.v33i01.33014926.

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Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix facto
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Kumar, Vikas, Arun K. Pujari, Vineet Padmanabhan, and Venkateswara Rao Kagita. "Group preserving label embedding for multi-label classification." Pattern Recognition 90 (June 2019): 23–34. http://dx.doi.org/10.1016/j.patcog.2019.01.009.

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Huang, Kuan-Hao, and Hsuan-Tien Lin. "Cost-sensitive label embedding for multi-label classification." Machine Learning 106, no. 9-10 (2017): 1725–46. http://dx.doi.org/10.1007/s10994-017-5659-z.

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Huang, Jun, Qian Xu, Xiwen Qu, Yaojin Lin, and Xiao Zheng. "Improving Multi-Label Learning by Correlation Embedding." Applied Sciences 11, no. 24 (2021): 12145. http://dx.doi.org/10.3390/app112412145.

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In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such
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Kumar, Vikas, Arun K. Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, and Venkateswara Rao Kagita. "Multi-label classification using hierarchical embedding." Expert Systems with Applications 91 (January 2018): 263–69. http://dx.doi.org/10.1016/j.eswa.2017.09.020.

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Dissertations / Theses on the topic "Multi-label embedding"

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Wang, Qian. "Zero-shot visual recognition via latent embedding learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html.

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Traditional supervised visual recognition methods require a great number of annotated examples for each concerned class. The collection and annotation of visual data (e.g., images and videos) could be laborious, tedious and time-consuming when the number of classes involved is very large. In addition, there are such situations where the test instances are from novel classes for which training examples are unavailable in the training stage. These issues can be addressed by zero-shot learning (ZSL), an emerging machine learning technique enabling the recognition of novel classes. The key issue i
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Huang, Kuan-Hao, and 黃冠豪. "Cost-sensitive Label Embedding for Multi-label Classification." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/05626650270566576330.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>104<br>Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, but they can suffer from bad performance with respect to other cost functions. In this paper, we resolve the performance issue by proposing a novel cost-sensitive LE algorithm that takes the cost function of interest into account. The proposed al
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Chiu, Hsien-Chun, and 邱顯鈞. "Multi-label Classification with Feature-aware Cost-sensitive Label Embedding." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/fy6vw4.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>Multi-label classification (MLC) is an important learning problem where each instance is annotated with multiple labels. Label embedding (LE) is an important family of methods for MLC that extracts and utilizes the latent structure of labels towards better performance. Within the family, feature- aware LE methods, which jointly consider the feature and label information during extraction, have been shown to reach better performance than feature- unaware ones. Nevertheless, current feature-aware LE methods are not de- signed to flexibly adapt to different eval
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Book chapters on the topic "Multi-label embedding"

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Wang, Yaqiang, Feifei Yan, Xiaofeng Wang, Wang Tang, and Hongping Shu. "Label Embedding Enhanced Multi-label Sequence Generation Model." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_18.

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Kimura, Keigo, Mineichi Kudo, and Lu Sun. "Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49055-7_2.

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Kumar, Sanjay, and Reshma Rastogi. "Auxiliary Label Embedding for Multi-label Learning with Missing Labels." In Computer Vision and Machine Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7867-8_42.

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Wang, Xidong, Jun Li, and Jianhua Xu. "A Label Embedding Method for Multi-label Classification via Exploiting Local Label Correlations." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36802-9_19.

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Liu, Yang, Guohua Dong, and Zhonglei Gu. "Sparse Multi-label Bilinear Embedding on Stiefel Manifolds." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01851-1_20.

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Li, Dan, Yunqian Li, Jun Li, and Jianhua Xu. "A Label Embedding Method via Conditional Covariance Maximization for Multi-label Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39821-6_32.

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Zhang, Xiangrong, Shouping Shan, Jing Gu, Xu Tang, and Licheng Jiao. "Multi-label Aerial Image Classification via Adjacency-Based Label and Feature Co-embedding." In Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_33.

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Liang, Huadong, Dengdi Sun, Zhuanlian Ding, and Meiling Ge. "Protein Function Prediction Using Multi-label Learning and ISOMAP Embedding." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-49014-3_23.

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Vanegas, Jorge A., Hugo Jair Escalante, and Fabio A. González. "Semi-supervised Online Kernel Semantic Embedding for Multi-label Annotation." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_83.

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Gao, Kaisheng, Jing Zhang, and Cangqi Zhou. "Semi-supervised Graph Embedding for Multi-label Graph Node Classification." In Web Information Systems Engineering – WISE 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34223-4_35.

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Conference papers on the topic "Multi-label embedding"

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Gu, Qiliang, Shuo Zhao, Jianqiang Zhang, Gongpeng Song, and Qin Lu. "MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831836.

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Xia, Peng, Di Xu, Ming Hu, Lie Ju, and Zongyuan Ge. "LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-Tailed Multi-Label Visual Recognition." In Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.alvr-1.3.

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Cheniki, Nasredine, Vidas Daudaravicius, Abdelfettah Feliachi, Didier Hardy, and Marc Wilhelm Küster. "Multi-Property Multi-Label Documents Metadata Recommendation based on Encoder Embeddings." In Proceedings of the Natural Legal Language Processing Workshop 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.nllp-1.19.

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Wertz, Lukas, Jasmina Bogojeska, Katsiaryna Mirylenka, and Jonas Kuhn. "Evaluating Pre-Trained Sentence-BERT with Class Embeddings in Active Learning for Multi-Label Text Classification." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-short.45.

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Tang, Lin, Lin Liu, and Jianhou Gan. "Multi-Label Topic Model Conditioned on Label Embedding." In 2019 IEEE International Conference on Computer Science and Educational Informatization (CSEI). IEEE, 2019. http://dx.doi.org/10.1109/csei47661.2019.8938881.

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Wang, Lichen, Zhengming Ding, and Yun Fu. "Adaptive Graph Guided Embedding for Multi-label Annotation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/388.

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Multi-label annotation is challenging since a large amount of well-labeled training data are required to achieve promising performance. However, providing such data is expensive while unlabeled data are widely available. To this end, we propose a novel Adaptive Graph Guided Embedding (AG2E) approach for multi-label annotation in a semi-supervised fashion, which utilizes limited labeled data associating with large-scale unlabeled data to facilitate learning performance. Specifically, a multi-label propagation scheme and an effective embedding are jointly learned to seek a latent space where unl
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Peng, Cheng-Lun, An Tao, and Xin Geng. "Label Embedding Based on Multi-Scale Locality Preservation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/364.

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Label Distribution Learning (LDL) fits the situations well that focus on the overall distribution of the whole series of labels. The numerical labels of LDL satisfy the integrity probability constraint. Due to LDL's special label domain, existing label embedding algorithms that focus on embedding of binary labels are thus unfit for LDL. This paper proposes a specially designed approach MSLP that achieves label embedding for LDL by Multi-Scale Locality Preserving (MSLP). Specifically, MSLP takes the locality information of data in both the label space and the feature space into account with dif
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Chiu, Hsien-Chun, and Hsuan-Tien Lin. "Multi-Label Classification with Feature-Aware Cost-Sensitive Label Embedding." In 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2018. http://dx.doi.org/10.1109/taai.2018.00018.

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Gong, Xiuwen, Dong Yuan, and Wei Bao. "Fast Multi-label Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/335.

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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. More importantly, much of the literature has already shown that the binary relevance (BR) method is usually good enough for some applications. Unfortunately, BR runs slowly due to its linear dependence on the size of the input data. The goal of this paper is
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Niu, Sijia, Qian Xu, Pengfei Zhu, Qinghua Hu, and Hong Shi. "Coupled Dictionary Learning for Multi-label Embedding." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852201.

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