RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Cluster indicator decomposition for efficient matrix factorization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Robust integrated locally linear embedding
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Non-negative and sparse spectral clustering
Pattern Recognition
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Laplacian embedding provides a low dimensional representation for a matrix of pairwise similarity data using the eigenvectors of the Laplacian matrix. The true power of Laplacian embedding is that it provides an approximation of the Ratio Cut clustering. However, Ratio Cut clustering requires the solution to be {\it nonnegative}. In this paper, we propose a new approach, nonnegative Laplacian embedding, which approximates Ratio Cut clustering in a more direct way than traditional approaches. From the solution of our approach, clustering structures can be read off directly. We also propose an efficient algorithm to optimize the objective function utilized in our approach. Empirical studies on many real world datasets show that our approach leads to more accurate Ratio Cut solution and improves clustering accuracy at the same time.