Semi-supervised heterogeneous fusion for multimedia data co-clustering
Co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2013
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5231 https://ink.library.smu.edu.sg/context/sis_research/article/6234/viewcontent/Semi_Supervised_Heterogeneous_Fusion___TKDE_2014.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by the empirical weighting of the multi-modal features. In this paper, we propose a generalized form of Heterogeneous Fusion Adaptive Resonance Theory, called GHF-ART, for co-clustering of large-scale web multimedia documents. By extending the two-channel Heterogeneous Fusion ART (HF-ART) to multiple channels, GHF-ART is designed to handle multimedia data with an arbitrarily rich level of meta-information. For handling short and noisy text, GHF-ART does not learn directly from the textual features. Instead, it identifies key tags by learning the probabilistic distribution of tag occurrences. More importantly, GHF-ART incorporates an adaptive method for effective fusion of multi-modal features, which weights the features of multiple data sources by incrementally measuring the importance of feature modalities through the intra-cluster scatters. Extensive experiments on two web image data sets and one text document set have shown that GHF-ART achieves significantly better clustering performance and is much faster than many existing state-of-the-art algorithms. |
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