Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Convex Optimization
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Local learning regularized nonnegative matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning search tasks in queries and web pages via graph regularization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Adaptive loss minimization for semi-supervised elastic embedding
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Semi-supervised learning has witnessed increasing interest in the past decade. One common assumption behind semi-supervised learning is that the data labels should be sufficiently smooth with respect to the intrinsic data manifold. Recent research has shown that the features also lie on a manifold. Moreover, there is a duality between data points and features, that is, data points can be classified based on their distribution on features, while features can be classified based on their distribution on the data points. However, existing semi-supervised learning methods neglect these points. Based on the above observations, in this paper, we present a dual regularization, which consists of two graph regularizers and a co-clustering type regularizer. In detail, the two graph regularizers consider the geometric structure of the data points and the features respectively, while the co-clustering type regularizer takes into account the duality between data points and features. Furthermore, we propose a novel transductive classification framework based on dual regularization, which can be solved by alternating minimization algorithm and its convergence is theoretically guaranteed. Experiments on benchmark semi-supervised learning data sets demonstrate that the proposed methods outperform many state of the art transductive classification methods.