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Erschienen in:
Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Tensor Learning in Multi-view Kernel PCA

verfasst von : Lynn Houthuys, Johan A. K. Suykens

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018

Verlag: Springer International Publishing

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Abstract

In many real-life applications data can be described through multiple representations, or views. Multi-view learning aims at combining the information from all views, in order to obtain a better performance. Most well-known multi-view methods optimize some form of correlation between two views, while in many applications there are three or more views available. This is usually tackled by optimizing the correlations pairwise. However, this ignores the higher-order correlations that could only be discovered when exploring all views simultaneously. This paper proposes novel multi-view Kernel PCA models. By introducing a model tensor, the proposed models aim to include the higher-order correlations between all views. The paper further explores the use of these models as multi-view dimensionality reduction techniques and shows experimental results on several real-life datasets. These experiments demonstrate the merit of the proposed methods.

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Vorheriges Kapitel High-Resolution Generative Adversarial Neural Networks Applied to Histological Images Generation
Nächstes Kapitel ACM: Learning Dynamic Multi-agent Cooperation via Attentional Communication Model
Fußnoten
1
To calculate the NMI, and hence asses the performance, the labels of the dataset are used. However, notice that they are never used in the training or validation phase of KM, KSC or the proposed multi-view KPCA models.
 
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Metadaten
Titel
Tensor Learning in Multi-view Kernel PCA
verfasst von
Lynn Houthuys
Johan A. K. Suykens
Copyright-Jahr
2018
Buch
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01421-6_21