A Survey on Multi-view Learning

By Chang Xu, Dacheng Tao, Chao Xu. 2013

1,241 citations recorded by the source corpus.

In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In trying to organize and highlight similarities and differences between the variety of multi-view learning approaches, we review a number of representative multi-view learning algorithms in different areas and classify them into three groups: 1) co-training, 2) multiple kernel learning, and 3) subspace learning. Notably, co-training style algorithms train alternately to maximize the mutual agreement on two distinct views of the data; multiple kernel learning algorithms exploit kernels that naturally correspond to different views and combine kernels either linearly or non-linearly to improve learning performance; and subspace learning algorithms aim to obtain a latent subspace shared by multiple views by assuming that the input views are generated from this latent subspace. Though there is significant variance in the approaches to integrating multiple views to improve learning performance, they mainly exploit either the consensus principle or the complementary principle to ensure the success of multi-view learning. Since accessing multiple views is the fundament of multi-view learning, with the exception of study on learning a model from multiple views, it is also valuable to study how to construct multiple views and how to evaluate these views. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is rendered more effective, more promising, and has better generalization ability than single-view learning.

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

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Citations in the corpus

Semi-Supervised Sequence Modeling with Cross-View Training (2018, methodology): “Multi-view learning on data where features can be separated into distinct subsets has been well studied (Xu et al., 2013).”
A Survey of Machine Learning for Big Code and Naturalness (2017, neutral): “Multi-view [193] and multi-modal learning (e.”
Contrastive Multiview Coding (2019, background): “The core ideas that we build on: contrastive learning, mutual information maximization, and deep representation learning, are not new and have been explored in the literature on representation and multiview learning [34, 59, 2].”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “Multimodal/Multiview learning [112] is a rising technique which also found considerable support in AEs.”
Conditional variance penalties and domain shift robustness (2017, background): “More generally, the idea of exploiting various observations of the same underlying object is related to multi-view learning (Xu et al., 2013).”
Adversarial Continual Learning (2020, background): “In the machine learning literature, multi-view learning, aims at constructing and/or using different views or modalities for better learning performances [3,39].”
A Comprehensive Survey on Transfer Learning (2019, methodology): “There are several strategies adopted in multiview learning such as subspace learning, multikernel learning, and co-training [13], [14].”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “…learning methods are related to HDA/HTL as discussed also in Section 3.3, however the main difference being that while multi-view learning [250, 239] assumes that during training multi-view examples are available, in the case of HDA [203, 237, 120, 200, 264], this assumption rarely holds…”
What Makes Multimodal Learning Better than Single (Provably) (2021, background): “Besides, previous multi-view analysis [37, 1, 45, 13] typically assumes that each view alone is sufficient to predict the target accurately, which may not hold in our multi-modal setting.”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “For a comprehensive review of multi-view learning, please r ef r to [1].”
Deep Co-Training for Semi-Supervised Image Recognition (2018, background): “But dual-view is only a special case of multi-view learning, and multi-view co-training has also been studied for other problems [30, 31].”
Recurrent Fusion Network for Image Captioning (2018, background): “Our RFNet also relates to information fusion, multi-view learning [29], and ensemble learning [30].”
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].”