Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian modeling of facial similarity
Proceedings of the 1998 conference on Advances in neural information processing systems II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Classification of bio-data with small data set using additive factor model and SVM
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Currently, high-dimensional data such as image data is widely used in the domain of pattern classification and signal processing. When using high-dimensional data, feature analysis methods such as PCA (principal component analysis) and LDA (linear discriminant analysis) are usually required in order to reduce memory usage or computational complexity as well as to increase classification performance. We propose a feature analysis method for dimension reduction based on a data generation model that is composed of two types of factors: class factors and environment factors. The class factors, which are prototypes of the classes, contain important information required for discriminating between various classes. The environment factors, which represent distortions of the class prototypes, need to be diminished for obtaining high class separability. Using the data generation model, we aimed to exclude environment factors and extract low-dimensional class factors from the original data. By performing computational experiments on artificial data sets and real facial data sets, we confirmed that the proposed method can efficiently extract low-dimensional features required for classification and has a better performance than the conventional methods.