Illumination Planning for Object Recognition Using Parametric Eigenspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Journal of Cognitive Neuroscience
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
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This paper introduces a new non-linear feature extraction technique based on Canonical Correlation Analysis (CCA) with applications in regression and object recognition. The non-linear transformation of the input data is performed using kernel-methods. Although, in this respect, our approach is similar to other generalized linear methods like kernel-PCA, our method is especially well suited for relating two sets of measurements. The benefits of our method compared to standard feature extraction methods based on PCA will be illustrated with several experiments from the field of object recognition and pose estimation.