Results 231 to 240 of about 5,726,851 (263)

A Category-Driven Contrastive Recovery Network for Double Incomplete Multi-view Multi-label Classification

IEEE Transactions on Multimedia
Jie Wen, Dongxia Chang, Fu Xiao
exaly   +3 more sources

A Framework of the Semi-supervised Multi-label Classification with Non-uniformly Distributed Incomplete Labels

Lecture Notes in Computer Science, 2016
In real world applications, the problem of incomplete labels is frequently encountered. These incomplete labels decrease the accuracy of the supervised classification model because of a lack of negative examples and the non-uniform distribution of the missing labels.
Chih-Heng Chung, Bi-Ru Dai
exaly   +3 more sources

Learning consistent representation for incomplete multi-view weak multi-label classification

Neurocomputing
Dkb Shuai, Shaodong Cui
exaly   +3 more sources

Task-augmented cross-view imputation network for partial multi-view incomplete multi-label classification

Neural Networks
In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplete training data due to limitations in data collection and unreliable annotation processes. The absence of multi-view features impairs the comprehensive understanding of samples, omitting crucial details essential for classification. To address this issue,
Jie Wen, Jie Wen, Wai Keung Wong
exaly   +5 more sources

A Deep Self-learning Classification Framework for Incomplete Medical Patents with Multi-label

2019
The classification of medical patents play an important role for pharmaceutical company, since medical patens with well labeled can significantly accelerate the process of new drug research. The previous studies using machine learning methods focus on classification the medical patents with single label.
Mengzhen Luo, Xiaoyu Shi 0001, Qianqian Ji, Mingsheng Shang 0001, Xianbo He, Weiguo Tao   +5 more
openaire   +1 more source

Confidence-Enhanced Dual-Space Semantic Alignment for partial multi-view incomplete multi-label classification

Knowledge-Based Systems
Yinghao Ye, Wulin Xie, Xiaohuan Lu
exaly   +2 more sources

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

Proceedings of the AAAI Conference on Artificial Intelligence
As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification.
Chengliang Liu 0003, Jinlong Jia, Jie Wen 0001, Yabo Liu, Xiaoling Luo 0001, Chao Huang 0008, Yong Xu 0001   +6 more
openaire   +1 more source

Transformed Schatten-1 penalty based full-rank latent label learning for incomplete multi-label classification

Information Sciences, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tingquan Deng, Qingwei Jia, Jingyu Wang, Hamido Fujita   +3 more
openaire   +3 more sources

Incomplete Multi-View Multi-Label Classification via Diffusion-Guided Redundancy Removal

Proceedings of the AAAI Conference on Artificial Intelligence
Incomplete multi-view multi-label classification aims to accurately predict labels for each sample in the face of some missing views. Due to its widespread presence in real-world scenarios, it has become an extensively researched topic. In addition to the challenges brought by missing views, it also encounters issues caused by redundant views, whose ...
Shilong Ou, Zhe Xue, Lixiong Qin, Yawen Li 0001, Meiyu Liang, Junjiang Wu, Xuyun Zhang, Amin Beheshti, Yuankai Qi   +8 more
openaire   +2 more sources

Disentangling Consistent and Specific Information for Double Incomplete Multi-View Multi-Label Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
As a prominent research topic, multi-view multi-label classification (MvMlC) aims to assign multiple labels to samples by integrating information from various perspectives. However, in real-world scenarios, MvMlC frequently faces the learning challenge of data with missing views and labels, typically resulting from sensor malfunctions, or the costly ...
Jie Wen, Lian Zhao, Xiaohuan Lu, Chengliang Liu, Li Shen, Chao Huang, Yong Xu   +6 more
openaire   +2 more sources