Discriminative concept factorization for data representation
Neurocomputing
Locality-constrained concept factorization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Clustering and understanding documents via discrimination information maximization
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Relational co-clustering via manifold ensemble learning
Proceedings of the 21st ACM international conference on Information and knowledge management
Generalized locality preserving Maxi-Min Margin Machine
Neural Networks
Regularized soft K-means for discriminant analysis
Neurocomputing
Feature selection for unsupervised learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
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Previous studies have demonstrated that document clustering performance can be improved significantly in lower dimensional linear subspaces. Recently, matrix factorization-based techniques, such as Nonnegative Matrix Factorization (NMF) and Concept Factorization (CF), have yielded impressive results. However, both of them effectively see only the global euclidean geometry, whereas the local manifold geometry is not fully considered. In this paper, we propose a new approach to extract the document concepts which are consistent with the manifold geometry such that each concept corresponds to a connected component. Central to our approach is a graph model which captures the local geometry of the document submanifold. Thus, we call it Locally Consistent Concept Factorization (LCCF). By using the graph Laplacian to smooth the document-to-concept mapping, LCCF can extract concepts with respect to the intrinsic manifold structure and thus documents associated with the same concept can be well clustered. The experimental results on TDT2 and Reuters-21578 have shown that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and mutual information.