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PROJECT TITLE :

Conditional Random Fields for Multiview Sequential Data Modeling

ABSTRACT:

In recent years, there has been an increased emphasis placed on Machine Learning in multiview learning. On the other hand, the majority of the currently available multiview learning methods are unable to directly deal with multiview sequential data, in which the inherent dynamical structure is frequently ignored. In particular, the vast majority of conventional multiview Machine Learning methods make the assumption that the components present at various time slices within a sequence can be considered independent of one another. In order to find a solution to this issue, we have proposed a new multiview discriminant model that is called multiview CRF. This model is based on conditional random fields (CRFs), which are used to model multiview sequential data. It retains the benefits of CRFs, which establish a relationship between the items in each sequence, which it inherited. In addition, the multiview CRF not only takes into consideration the relationship between the various views by incorporating specific features designed on the CRFs for multiview data, but it also takes into consideration and captures the correlation between the features that are present in the same view. In particular, it is possible to reuse certain features or divide them up into different views in order to construct an adequate size for the feature space. This helps to avoid underfitting problems, which are caused by feature space that is too small, as well as overfitting problems, which are caused by feature space that is too large. We utilize the stochastic gradient method to make our model run more quickly so that we can deal with large amounts of data. The experimental results on the text and video data clearly demonstrate that the proposed model is superior to its competitors.


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