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Cross-Domain Feature Learning in Multimedia
In the Web 2.0 era, a huge number of media data, such as text, image/video, and social interaction information, have been generated on the social media sites (e.g., Facebook, Google, Flickr, and YouTube).
These media data can be effectively adopted for many applications (e.g., image/video annotation, image/video retrieval, and event classification) in multimedia.
However, it is difficult to design an effective feature representation to describe these data because they have multi-modal property (e.g., text, image, video, and audio) and multi-domain property (e.g., Flickr, Google, and YouTube).
To deal with these issues, we propose a novel cross-domain feature learning (CDFL) algorithm based on stacked denoising auto-encoders. By introducing the modal correlation constraint and the cross-domain constraint in conventional auto-encoder, our CDFL can maximize the correlations among different modalities and extract domain invariant semantic features simultaneously.
To evaluate our CDFL algorithm, we apply it to three important applications: sentiment classification, spam filtering, and event classification.
Comprehensive evaluations demonstrate the encouraging performance of the proposed approach.


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