One of the major challenges encountered by current face recognition techniques lies in the
difficulties of handling varying poses, ie, recognition of faces in arbitrary in-depth rotations …

In this paper, some drawbacks of original kernel independent component analysis (KICA)
and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate …

Multiresolution ideas, notably the wavelet transform, have been proved quite useful for
analyzing the information content of facial images. Numerous papers and research articles …

The low-dimensional representation of high-dimensional data and the concise description of
its intrinsic structures are central problems in data analysis. In this paper, an unsupervised …

Recognition in uncontrolled situations is one of the most important bottlenecks for practical
face recognition systems. In particular, few researchers have addressed the challenge to …

In this article, the statistical modeling and online monitoring of nonlinear batch processes
are addressed on the basis of the kernel technique. First, the article analyzes the …

Classification of high-dimensional statistical data is usually not amenable to standard
pattern recognition techniques because of an underlying small sample size problem. To …

Although various discriminant analysis approaches have been used in content-based image
retrieval (CBIR) application, there have been relatively few concerns with kernel-based …

To overcome the disadvantage of linear dissimilarity analysis (DISSIM) when monitoring
nonlinear processes, a kernel dissimilarity analysis algorithm, termed KDISSIM here, is …

Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis,
especially for object representation and recognition. NMF requires the object tensor (with …