Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
The Balanced Accuracy and Its Posterior Distribution
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Information Forensics and Security - Part 2
IEEE Transactions on Neural Networks
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The medical analysis of human brain tumours commonly relies on indirect measurements. Among these, magnetic resonance imaging (MRI) and spectroscopy (MRS) predominate in clinical settings as tools for diagnostic assistance. Pattern recognition (PR) methods have successfully been used in this task, usually interpreting diagnosis as a supervised classification problem. In MRS, the acquired spectral signal can be analyzed in an unsupervised manner to extract its constituent sources. Recently, this has been successfully accomplished using Non-negative Matrix Factorization (NMF) methods. In this paper, we present a method to introduce the available class information into the unsupervised source extraction process of a convex variant of NMF. Novel techniques to generate diagnostic predictions for new, unseen spectra using the proposed Discriminant Convex-NMF are also described and experimentally assessed.