Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
Projective nonnegative matrix factorization for image compression and feature extraction
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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A new matrix factorization algorithm which combines two recently proposed nonnegative learning techniques is presented. Our new algorithm, α -PNMF, inherits the advantages of Projective Nonnegative Matrix Factorization (PNMF) for learning a highly orthogonal factor matrix. When the Kullback-Leibler (KL) divergence is generalized to α -divergence, it gives our method more flexibility in approximation. We provide multiplicative update rules for α -PNMF and present their convergence proof. The resulting algorithm is empirically verified to give a good solution by using a variety of real-world datasets. For feature extraction, α -PNMF is able to learn highly sparse and localized part-based representations of facial images. For clustering, the new method is also advantageous over Nonnegative Matrix Factorization with α -divergence and ordinary PNMF in terms of higher purity and smaller entropy.