Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Generalized mutual interdependence analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Mutual interdependence analysis (MIA)
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
| Hi-index | 0.01 |
Recently, mutual interdependence analysis (MIA) has been successfully used to extract representations, or ''mutual features'', accounting for samples in the class. For example, a mutual feature is a face signature under varying illumination conditions or a speaker signature under varying channel conditions. A mutual feature is a linear regression that is equally correlated with all samples of the input class. Previous work discussed two equivalent definitions of this problem and a generalization of its solution called generalized MIA (GMIA). Moreover, it showed how mutual features can be computed and employed. This paper uses a parametrized version GMIA(@l) to pursue a deeper understanding of what GMIA features really represent. It defines a generative signal model that is used to interpret GMIA(@l) and visualize its difference to MIA, principal and independent component analysis. Finally, we analyze the effect of @l on the feature extraction performance of GMIA(@l) in two standard pattern recognition problems: illumination-independent face recognition and text-independent speaker verification.