Abstract:
Machine learning is increasingly using multiview learning. Most multiview learning methods cannot directly handle multiview sequential data, which often ignores dynamical structure. Most traditional multiview machine learning methods assume that sequence items at different time slices are independent. We propose a multiview discriminant model based on conditional random fields (CRFs) to model multiview sequential data, called multiview CRF, to solve this problem. It benefits from CRFs that link sequence items. The multiview CRF also takes into account the correlation between features from the same view by adding features designed for multiview data. To create a suitable feature space, some features can be reused or divided into views. This prevents underfitting and overfitting due to feature space size. Our stochastic gradient model speeds up large-scale data. The proposed model outperforms the text and video data.
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