Machine Learning
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
A survey on wireless multimedia sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Improved feature reduction in input and feature spaces
Pattern Recognition
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Face detection is a key component in numerous computer vision applications. Most face detection algorithms achieve real-time performance by some form of dimensionality reduction of the input data, such as Principal Component Analysis. In this paper, we are exploring the emerging method of Random Projections (RP), a data independent linear projection method, for dimensionality reduction in the context of face detection. The benefits of using random projections include computational efficiency that can be obtained by implementing matrix multiplications with a small number of integer additions or subtractions. The computational savings are of great significance in resource constrained environments, such as wireless video sensor networks. Experimental results suggest that RP can achieve performance that is comparable to that obtained with traditional dimensionality reduction techniques for face detection using support vector machines.