A survey of multi-view machine learning
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Cites background from "A survey of multi-view machine lear..."
...• Finally, Multiview Learning [417] constructs different views of the object as per the information contained in the different data sources ( Fig....
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...• Finally, Multiview Learning [396] constructs different views of the object as per the information contained in the different data sources (Figure 13....
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...Multiview Learning devises strategies to jointly optimize ML models learned from the aforementioned views to enhance the generalization performance, specially in those applications with weak data supervision and hence, prone to model overfitting....
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679 citations
References
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13,246 citations
"A survey of multi-view machine lear..." refers methods in this paper
...Loosely speaking, a concept class C is PAC-learnable by a learner L using a hypothesis space H if, for any target concept in C, L will with probability at least (1 - d) output a hypothesis whose error is less than or equal to ; after training with a reasonable number of examples and performing a reasonable amount of computation [27]....
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6,122 citations
"A survey of multi-view machine lear..." refers background or methods in this paper
...Canonical correlation analysis, first proposed by Hotelling [21], works on a paired data set (e....
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...Note that, CCA [21] and Bayesian co-training [54] also belong to the co-regularization style category....
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...Canonical correlation analysis is an early and classical method for multi-view dimensionality reduction by learning subspaces jointly from different views [21]....
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...Canonical correlation analysis (CCA) [21] and cotraining [8] are two representative techniques in early studies of multi-view learning....
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"A survey of multi-view machine lear..." refers background or methods in this paper
...The co-training style algorithms are inspired by the co-training algorithm [8], which essentially involve an iterative procedure to exploit different views....
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...2.2 Effectiveness of co-training The original co-training algorithm was introduced by Blum and Mitchell [8] for semi-supervised classification that combines both labeled and unlabeled data under a twoview setting....
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...The original co-training algorithm was introduced by Blum and Mitchell [8] for semi-supervised classification that combines both labeled and unlabeled data under a twoview setting....
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...To justify the effectiveness of co-training, Blum and Mitchell [8] gave a PAC-style analysis....
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...Canonical correlation analysis (CCA) [21] and cotraining [8] are two representative techniques in early studies of multi-view learning....
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2,535 citations