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Unified Constraint Propagation on Multi-View Data [PDF]

Proceedings of the AAAI Conference on Artificial Intelligence, 2013
This paper presents a unified framework for intra-view and inter-view constraint propagation on multi-view data. Pairwise constraint propagation has been studied extensively, where each pairwise constraint is defined over a pair of data points from a single view.
Zhiwu Lu 0001, Yuxin Peng 0001
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Co-training on multi-view unlabelled data

Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012
A novel co-training framework is proposed for object orientation estimation in a multi-camera network environment. The model is initialised using a small labelled dataset and then iteratively boosted using large amount of unlabelled data which are generated automatically from videos.
Michal Lewandowski, James Orwell
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Multi-view Clustering on Relational Data

2014
Clustering is a popular task in knowledge discovery. In this chapter we illustrate this fact with a new clustering algorithm that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices.
Francisco de A. T. de Carvalho, Yves Lechevallier, Thierry Despeyroux, Filipe M. de Melo   +3 more
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Feature selection with multi-view data: A survey

Information Fusion, 2019
Abstract This survey aims at providing a state-of-the-art overview of feature selection and fusion strategies, which select and combine multi-view features effectively to accomplish associated tasks. The existing literatures on feature selection approaches are classified into three categories including filter method, wrapper method, and embedded ...
Rui Zhang 0017, Feiping Nie 0001, Xuelong Li 0001, Xian Wei   +3 more
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Multi-view 3D scanned data registration

Proceedings of the 2008 C3S2E conference on - C3S2E '08, 2008
We propose a new algorithm for registering 3D scans obtained from different views of an object. Our work differs from the popular ICP based approach since we minimize error in signed distance function instead of squared distance between sampled surface points themselves.
Sushil Bhakar, Ran Wang, Sudhir P. Mudur
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Multi-View Concept Learning for Data Representation

IEEE Transactions on Knowledge and Data Engineering, 2015
Real-world datasets often involve multiple views of data items, e.g., a Web page can be described by both its content and anchor texts of hyperlinks leading to it; photos in Flickr could be characterized by visual features, as well as user contributed tags. Different views provide information complementary to each other.
Ziyu Guan, Lijun Zhang 0005, Jinye Peng 0001, Jianping Fan 0001   +3 more
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Binary spectral clustering for multi-view data

Information Sciences
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xueming Yan, Guo Zhong, Yaochu Jin, Xiaohua Ke, Fenfang Xie, Guoheng Huang   +5 more
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A survey on representation learning for multi-view data

Neural Networks
Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering
Yalan Qin, Xinpeng Zhang 0001, Shui Yu 0001, Guorui Feng   +3 more
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Multi-view Semantic Learning for Data Representation

2015
Many real-world datasets are represented by multiple features or modalities which often provide compatible and complementary information to each other. In order to obtain a good data representation that synthesizes multiple features, researchers have proposed different multi-view subspace learning algorithms.
Peng Luo 0007, Jinye Peng 0001, Ziyu Guan, Jianping Fan 0001   +3 more
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Outlier-robust multi-view clustering for uncertain data

Knowledge-Based Systems, 2021
Abstract Nowadays, multi-view clustering is drawn more and more attention in the area of machine learning because real-world datasets frequently consist of multiple views. Moreover, it provides complementary and consensus information across multiple views. So, owing to the efficacy of revealing the concealed patterns in uncertain data, multiple views
Krishna Kumar Sharma, Ayan Seal
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