Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear subspace learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). All of these methods consider an n1 × n2 ...

As targeted advertising becomes prevalent in a wide variety of media vehicles, planning models become increasingly important to ad networks that need to match ads to appropriate audience segments, provide a high quality of service (meet advertisers’ goals), and ensure that ad serving opportunities are not wasted. We define Guaranteed Targeted Display Advertising (GTDA) as a class of media vehic...

When Einstein was thinking about the theory of general relativity based on the elimination of especial relativity constraints (especially the geometric relationship of space and time), he understood the first limitation of especial relativity is ignoring changes over time. Because in especial relativity, only the curvature of the space was considered. Therefore, tensor calculations should be to...

The Linear discriminant analysis (LDA) can be generalized into a nonlinear form ─ kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experime...

background: unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. in the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population.   methods : in this cross-sectional study, 887 pregnant mothers referring to health centers in khorramabad, iran, in 2012 were selec...

Tensor completion is an important topic in the area of image processing and computer vision research, which is generally built on extraction of the intrinsic structure of the tensor data. Drawing on this fact, action classification, relying heavily on the extracted features of high-dimensional tensors, may indeed benefit from tensor completion techniques. In this paper, we propose a low-rank te...

Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improve...

classical lbp such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. in this paper, we introduce an improved lbp algorithm to solve these problems that utilizes fast pca algorithm for reduction of vector dimensions of extracted features. in other words, proffer method (fast pca+lbp) is an improved lbp algorithm that is extracted ...