Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …

Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …

Selective laser melting (SLM) has been attracting a growing interest in different industrial
sectors as it is one of the key technologies for metal additive manufacturing (AM). Despite …

This paper introduces a multilinear principal component analysis (MPCA) framework for
tensor object feature extraction. Objects of interest in many computer vision and pattern …

Abstract Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction
and dimension reduction. It has been used widely in many applications involving high …

Increasingly large amount of multidimensional data are being generated on a daily basis in
many applications. This leads to a strong demand for learning algorithms to extract useful …

We present a new approach, called local discriminant embedding (LDE), to manifold
learning and pattern classification. In our framework, the neighbor and class relations of data …

We consider the problem of computing low rank approximations of matrices. The novelty of
our approach is that the low rank approximations are on a sequence of matrices. Unlike the …

In solving complex visual learning tasks, adopting multiple descriptors to more precisely
characterize the data has been a feasible way for improving performance. The resulting data …