(MVML-IVL) for solution and it is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, label correlation matrix, low-rank assumption matrix, multi-granularity label correlation, and complementary ...
Multi-label learning 多标签学习是多任务学习中的一种,建模多个label之间的相关性,同时对多个label进行建模,多个类别之间共享相同的数据/特征。 Multi-class learning多类别学习是多标签学习任务中的一种,对多个相互独立的类别(classes)进行建模。 【自己理解】 4. Multi-scale learning 多尺度学习: 图像摘自Spatial ...
Data set with incomplete information, multi-granularity label correlation when label-specific features and complementarity information provided is ubiquitous in real-world applications. In this paper, we develop a new multi-view multi-label learning with incomplete views and labels (MVML-IVL) for solut...
[9] Qiaoyu Tan, Guoxian Yu, Carlotta Domeniconi, Jun Wang, and Zili Zhang. Incomplete multi-view weak-label learning. In IJCAI, pages 2703–2709, 2018. [10] Changming Zhu, Duoqian Miao, Rigui Zhou, and Lai Wei. Improved multi-view multi-label learning with incomplete views and labels. ...
(MVL) can use the consensus and complementary information between different views to obtain better learning results, it has become an important research direction of machine learning [7], [8], [9], [10] and has been expanded to many fields, such as multi-view multi-label learning [11], ...
Multi-view learning has received much attention in machine learning, especially for multi-label and image classification [3], [4], [5]. Learning classification models from the fusion of multiple views increases the strength of the classification predictions as compared to independent views [6], [...
Code for paper: "DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification" in AAAI-2023 You can run "python final-DICNET_bestresults.py" to train model in semi-supervised case (in the paper 100% data for training) and get the best results...
5. Multi-label learning or Weak-label learning - Weak-label learning is an important branch of multi-label learning. 5.2 Access19 Multi-View Multi-Label Learning With View-Label-Specific Features(matlab) 5.3 The method in 1.4 is also a multi-label learning method. ...
Generative Models as a Data Source for Multiview Representation Learning 生成模型现在能够产生高度逼真的图像,这些图像看起来与它们所训练的数据几乎没有区别。这就提出了一个问题:如果我们有足够好的生成模型,我们还需要数据集吗?我们在从黑盒生成模型而不是直接从数据中学习通用的视觉表征的背景下研究这个问题。给...
Multi-view multi-instance multi-label learning based on collaborative matrix factorization WangS. et al. Multi-view clustering via late fusion alignment maximizationView more references Cited by (68) Learnable graph convolutional network and feature fusion for multi-view learning 2023, Information Fusion...