Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Concept decompositions for large sparse text data using clustering
Machine Learning
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
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We propose a method called Topic Graph based NMF for Transfer Learning (TNT) based on Non-negative Matrix Factorization (NMF). Since NMF learns feature vectors to approximate the given data, the proposed approach tries to preserve the feature space which is spanned by the feature vectors to realize transfer learning. Based on the learned feature vectors in the source domain, a graph structure called topic graph is constructed, and the graph is utilized as a regularization term in the framework of NMF. We show that the proposed regularization term corresponds to maximizing the similarity between topic graphs in both domains, and that the term corresponds to the graph Laplacian of the topic graph. Furthermore, we propose a learning algorithm with multiplicative update rules and prove its convergence. The proposed method is evaluated over document clustering problem, and the results indicate that the proposed method improves performance via transfer learning.