Journal
MACHINE LEARNING
Volume 109, Issue 2, Pages 219-249Publisher
SPRINGER
DOI: 10.1007/s10994-019-05853-8
Keywords
Multi-view learning; Online learning; Bayesian subspace learning; Max-margin; Classification; Regression
Categories
Funding
- National Key Research and Development Program of China [2018YFB1004300]
- National Natural Science Foundation of China [U1811461, 61602449, U1836206, 61773361]
- Project of Youth Innovation Promotion Association CAS [2017146]
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Multi-view data have become increasingly popular in many real-world applications where data are generated from different information channels or different views such as image + text, audio + video, and webpage + link data. Last decades have witnessed a number of studies devoted to multi-view learning algorithms, especially the predictive latent subspace learning approaches which aim at obtaining a subspace shared by multiple views and then learning models in the shared subspace. However, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm which learns predictive subspace with the max-margin principle. Specifically, we first define the latent margin loss for classification or regression in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian passive-aggressive style. Finally, we extensively evaluate our model on several real-world data sets and the experimental results show that our models can achieve superior performance, compared with a number of state-of-the-art competitors.
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