Results 251 to 260 of about 21,136,484 (286)

Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification

2012 IEEE 12th International Conference on Data Mining, 2012
Multiple feature views arise in various important data classification scenarios. However, finding a consensus feature view from multiple feature views for a classifier is still a challenging task. We present a new classification framework using the multi-label correlation information to address the problem of simultaneously combining multiple feature ...
Zheng Fang 0013, Zhongfei (Mark) Zhang
openaire   +2 more sources

Robust Mapping Learning for Multi-view Multi-label Classification with Missing Labels

2017
The multi-label classification problem has generated significant interest in recent years. Typical scenarios assume each instance can be assigned to a set of labels. Most of previous works regard the original labels as authentic label assignments which ignore missing labels in realistic applications.
Weijieying Ren, Lei Zhang 0060, Bo Jiang 0002, Zhefeng Wang, Guangming Guo, Guiquan Liu   +5 more
openaire   +2 more sources

Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 2013
Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to
Yong Luo 0002, Dacheng Tao, Chang Xu 0002, Dongchen Li, Chao Xu 0006   +4 more
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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

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

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

Semi-Supervised Multi-view Multi-label Classification Based on Nonnegative Matrix Factorization

2017
Many real-world applications involve multi-label classification where each sample is usually associated with a set of labels. Although many methods have been proposed, most of them are just applicable to single-view data neglecting the complementary information among multiple views.
Guangxia Wang, Changqing Zhang 0002, Pengfei Zhu 0001, Qinghua Hu   +3 more
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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
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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

An effective bipartite graph fusion and contrastive label correlation for multi-view multi-label classification

Pattern Recognition
Dawei Zhao, Qingwei Gao, Yixiang Lu
exaly   +3 more sources