Results 241 to 250 of about 1,699,128 (261)

Contrastive Multi-View Kernel Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert space where samples can be linearly separated. Most kernel-based multi-view learning algorithms compute a kernel function aggregating and compressing the views into a single kernel.
Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Qing Liao, Yuanqing Xia   +4 more
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Multi-view Ensemble Learning

Proceedings of the Third International Symposium on Women in Computing and Informatics, 2015
Partitioning the feature set into non-empty subsets of features is the generalized task of feature subset selection. The subsets of features are collectively useful than a subset of the feature. The composition of classification models of the multiple views corresponding each subset of the feature-set can outperform a single-view classifier. Multi-view
Vipin Kumar, Sonajharia Minz
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Multi-view representation learning for multi-view action recognition

Journal of Visual Communication and Image Representation, 2017
This approach directly exploits the relationships among different action categories from different views.We bridge the gap of the sparsity representation of different actions from the different views.This approach explores the task of cross-view recognition.
Tong Hao, Dan Wu, Qian Wang, Jin-Sheng Sun   +3 more
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Uniform Projection for Multi-View Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Multi-view learning aims to integrate multiple data information from different views to improve the learning performance. The key problem is to handle the unconformities or distortions among view-specific samples or measurements of similarity or dissimilarity.
, Zhenyue Zhang, , Zheng Zhai, , Limin Li   +2 more
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Multi-View Learning With Incomplete Views

IEEE Transactions on Image Processing, 2015
One underlying assumption of the conventional multi-view learning algorithms is that all examples can be successfully observed on all the views. However, due to various failures or faults in collecting and pre-processing the data on different views, we are more likely to be faced with an incomplete-view setting, where an example could be missing its ...
Chang, Xu, Dacheng, Tao, Chao, Xu
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Attentive multi-view reinforcement learning

International Journal of Machine Learning and Cybernetics, 2020
The reinforcement learning process usually takes millions of steps from scratch, due to the limited observation experience. More precisely, the representation approximated by a single deep network is usually limited for reinforcement learning agents.
Yueyue Hu, Shiliang Sun, Xin Xu, Jing Zhao   +3 more
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Multi-view Transformation Learning

2018
In this chapter, we would propose two multi-view transformation learning algorithms to solve the classification problem. First of all, we consider the multi-view data have two kinds of manifold structures, i.e., class structure and view structure, then design a dual low-rank decomposition algorithm.
Zhengming Ding, Handong Zhao, Yun Fu
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Multi-view learning with Universum

Knowledge-Based Systems, 2014
The traditional Multi-view Learning (MVL) studies how to process patterns with multiple information sources. In practice, the MVL is proven to have a significant advantage over the Single-view Learning (SVL). But in most real-world cases, there are only single-source patterns to be dealt with and the existing MVL is unable to be directly applied.
Zhe Wang, Yujin Zhu, Wenwen Liu, Zhihua Chen, Daqi Gao   +4 more
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Implicit Weight Learning for Multi-View Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2023
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. In general, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in the description.
Feiping Nie, Shaojun Shi, Jing Li, Xuelong Li   +3 more
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