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Venue photos, as a new type of multimedia contents, are exploding on the Internet because users like to take photos and share with their friends in which venue they spent time and what impressed them there. Discovering a venue by a social photo is very useful for supplementing venue retrieval and recommendation. However, little research focused on fine-grained venue discovery by leveraging multimodal...
This work is originated from the MLSP 2014 Classification Challenge which tries to automatically detect subjects with schizophrenia and schizo-affective disorder by analyzing multi-modal features derived from magnetic resonance imaging (MRI) data. We employ Deep Neural Network (DNN)-based multi-view representation learning for combining multimodal features. The DNN-based multi-view models include...
We are interested in Greek folk music genre classification by resorting to canonical correlation analysis (CCA). Here, the genre is related to the place of origin of the song. The CCA learns a linear transformation of the song lyrics descriptors that is highly correlated with their genre labels as well as another linear transformation of the audio features extracted from music recordings, which is...
It has been previously shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning acoustic features from this multi-view data with canonical correlation analysis (CCA). In contrast with previous work based on linear or kernel CCA, we use the recently proposed deep CCA, where the functional form of the feature mapping...
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