A survey of multi-view machine learning

By Shiliang Sun. 2013

862 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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In Computer Vision and Pattern Recognition

In Representation & Self-Supervised Learning

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “• Finally, Multiview Learning [417] constructs diﬀerent views of the object as per the information contained in the diﬀerent data sources ( Fig.”
A Comprehensive Survey on Transfer Learning (2019, background): “Multiview learning [12] focuses on the machine learning problems with multiview data.”
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval (2018, background): “Owing to the explosive increase in multimedia data from a great variety of search engines and social media, crossmodal retrieval has become a compelling topic in recent years [20, 21, 22, 23, 24, 25, 29, 35, 36, 41, 42, 45].”
Doubly Aligned Incomplete Multi-view Clustering (2018, background): “In multi-view data, these different views often share some consistency and complementary information [Sun, 2013; Zhao et al. , 2017].”
A Survey of Multi-View Representation Learning (2016, neutral): “Third, [28] classifies the multiview learning algorithms into three different settings: co-training style, multiple kernel learning, and subspace learning; the survey of [4] provides a comprehensive review of CCA, effectiveness of co-training, and generalization error analysis for co-training and ot”
Label Informed Attributed Network Embedding (2017, background): “A more detailed review of multi-view learning can be referred to [32].”
AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “In ML, this is referred to as a multi-view setting [5], where each omic is regarded as a separate view.”
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, methodology): “Referring to principles of multi-view machine learning [33], we propose a Joint Representation Learning (JRL) approach as a general framework for recommendation, which builds representation learning on top of pair-wise learning to rank for top-N recommendation (Figure 1).”
Learning Multiple Views with Orthogonal Denoising Autoencoders (2016, background): “In many machine learning problems, data samples are collected from diverse sensors or described by various features and inherently have multiple disjoint feature sets (conventionally referred as views) [1,2].”
A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation (2015, background): “Support Vector Machine (SVM) is a well-known machine learning approach which has recently applied in the variety of fields such as computing, hydrology and environmental researches [45–49].”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, background): “Multi-view learning [19] for SSC is usually understood to be a generalization of co-training, without requiring explicit feature splits or the iterative mutual-”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “[49] provided a survey for multi-view machine learning and pointed out that multi-view learning is related to the machine learning problem with the data represented by multiple distinct feature sets.”
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021, background): “Sun (2013) Knowledge Distillation Knowledge distillation (Buciluˇa et al., 2006; Hinton et al., 2015) transfers the knowledge of “teacher” models to smaller “student” models through minimizing the KL divergence of prediction probability distribution be-tween the models.”
Deep multi-view learning methods: A review (2021, background): “These surveys focus on the problems of specific MVL methods, such as multi-view fusion [10, 27, 28], multi-modal learning [37, 38], multi-view clustering [26] and multi-view representation learning [39, 40].”
A hybrid SVM-FFA method for prediction of monthly mean global solar radiation (2015, methodology): “SVM is one of the soft computing learning algorithms which has recently applied in the variety of fields such as computing, hydrology, and environmental researches (Lu andWang 2005; Asefa et al. 2006; Ji and Sun 2013; Sun 2013).”