Speaker independent feature extraction is a critical problem in speech recognition. Oriented principal component analysis (OPCA) is a potential solution that can find a subspace r...
A novel kernel discriminant transformation (KDT) algorithm based on the concept of canonical differences is presented for automatic face recognition applications. For each individu...
Wen-Sheng Vincent Chu, Ju-Chin Chen, Jenn-Jier Jam...
In this paper, we introduce a method for estimating the statistically distinct neural responses in an sequence of functional magnetic resonance images (fMRI). The crux of our meth...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
— Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been...