Neural Computation
Spectral K-way ratio-cut partitioning and clustering
DAC '93 Proceedings of the 30th international Design Automation Conference
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Regularized clustering for documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking multiple topics for finding interesting articles
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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Transductive learning is proposed to incorporate both labeled and unlabeled examples into the learning process. Several methods have been developed and show encouraging performance. However, people may meet complicated classification tasks in real world applications, where one category contains multiple components. Traditional transductive learning algorithms are not very effective in such settings. In this paper, we propose a novel transductive learning approach called constrained local regularized transducer (CLRT) for multi-component category classification. CLRT is based on the local separable assumption that it is possible to build a linear predictor in one small area. We implement the assumption by minimizing a unified objective function, which can be optimized globally. Experiment results validate that CLRT can achieve satisfied performance robustly and efficiently.