Function-Function correlated multi-label protein function prediction over interaction networks
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous image classification and annotation via biased random walk on tri-relational graph
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Real web community based automatic image annotation
Computers and Electrical Engineering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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Image annotation is usually formulated as a multi-label semi-supervised learning problem. Traditional graph-based methods only utilize the data (images) graph induced from image similarities, while ignore the label (semantic terms) graph induced from label correlations of a multi-label image data set. In this paper, we propose a novel Bi-relational Graph (BG) model that comprises both the data graph and the label graph as subgraphs, and connect them by an additional bipartite graph induced from label assignments. By considering each class and its labeled images as a semantic group, we perform random walk on the BG to produce group-to-vertex relevance, including class-to-image and class-to-class relevances. The former can be used to predict labels for unannotated images, while the latter are new class relationships, called as Causal Relationships (CR), which are asymmetric. CR is learned from input data and has better semantic meaning to enhance the label prediction for unannotated images. We apply the proposed approaches to automatic image annotation and semantic image retrieval tasks on four benchmark multi-label image data sets. The superior performance of our approaches compared to state-of-the-art multi-label classification methods demonstrate their effectiveness.