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Uniform Projection for Multi-View Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017Multi-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 +2 more
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Multi-View Learning With Incomplete Views
IEEE Transactions on Image Processing, 2015One 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, 2020The 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 +3 more
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Multi-view Transformation Learning
2018In 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, 2014The 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 +4 more
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Implicit Weight Learning for Multi-View Clustering
IEEE Transactions on Neural Networks and Learning Systems, 2023Exploiting 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 +3 more
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Robust Multi-View Prototype Learning
2020 International Conference on Internet of Things and Intelligent Applications (ITIA), 2020In vision and machine learning, information fusion from multiple sensors can be regarded as multi-view learning paradigm to make use of the pairwise complementary information. Due to disturbed variances by illumination, equipment and environment, the collected data is frequently smeared by noises. Although there have been outlier-against works proposed,
Qing Tian +3 more
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Multi-view dynamic texture learning
2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016Dynamic texture (DT) provides a flexible and suitable tool for representing phenomena over space and time. We focus here on DT learning for multi-view domains, each of which is sufficient to learn the target concept. We make several contributions in this paper. First, we derive new features and then present their use in our description of DT.
Thanh Minh Nguyen, Q. M. Jonathan Wu
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Robust Multi-view Subspace Learning
2017By virtue of the increasingly large amount of various sensors, information about the same object can be collected from multiple views. These mutually enriched information can help many real-world applications, such as daily activity recognition in which both video cameras and on-body sensors are continuously collecting information.
Sheng Li, Yun Fu
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Biased Incomplete Multi-View Learning
Proceedings of the AAAI Conference on Artificial IntelligenceConsidering the ubiquitous phenomenon of missing views in multi-view data, incomplete multi-view learning is a crucial task in many applications. Existing methods usually follow an impute-then-predict strategy for handling this problem. However, they often assume that the view-missing patterns are uniformly random in multi-view data, which does not ...
Haishun Chen +4 more
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