Elements of information theory
Elements of information theory
Non-negative Matrix Factorization for Face Recognition
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
Local Non-Negative Matrix Factorization as a Visual Representation
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Local descriptors in application to the aging problem in face recognition
Pattern Recognition
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Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better with intuitive notions of parts of images. However, there has not been any systematic study to identify suitable distance measure for using NMF basis images for face recognition.In this article we evaluate the performance of 17 distance measures between feature vectors based on the result of the NMF algorithm for face recognition. Recognition experiments are performed using the MIT-CBCL database, CMU AMP Face Expression database and YaleB database.