Linear and kernel discriminant analyses are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analyses have been proposed to ove...
A number of learning machines used in information science are not regular, but rather singular, because they are non-identifiable and their Fisher information matrices are singula...
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...
Many settings of unsupervised learning can be viewed as quantization problems — the minimization of the expected quantization error subject to some restrictions. This allows the ...
Alex J. Smola, Robert C. Williamson, Sebastian Mik...
The ranking problem appears in many areas of study such as customer rating, social science, economics, and information retrieval. Ranking can be formulated as a classification pro...