In this paper we examine the use of a mathematical procedure, called Principal Component Analysis, in Recommender Systems. The resulting filtering algorithm applies PCA on user ratings and demographic data, aiming to improve various aspects of the recommendation process. After a brief introduction to PCA, we provide a discussion of the proposed PCADemog algorithm, along with possible ways of co...

Nowadays we are living in the information age with the fast development of computational technologies and modern facilities. Larger data sets are produced by experiments and computer simulations. In contrast to conventional scientific approaches where simple models are built to fit the data, automated procedures are urged to obtain insights into the core messages carried by the large volume of ...

The paper presents an automatic classification system, which discriminates the different types of single-layered clouds using Principal Component Analysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification technique, which is typically used for face recognition. PCA can be used to identify the image features called principal components. A principal comp...

The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint The process of rrecognizing facial expression is a hard task due to the several circumstances such as facial ...

Channel-state-information (CSI) feedback methods are considered, especially for massive or very large-scale multipleinput multiple-output (MIMO) systems. To extract essential information from the CSI without redundancy that arises from the highly correlated antennas, a receiver transforms (sparsifies) a correlated CSI vector to an uncorrelated sparse CSI vector by using a Karhunen-Loève transfo...

l=1 σlulv T l (1) ∀ l σl ∈ R, σl ≥ 0 (2) ∀ l, l 〈ul, ul′〉 = 〈vl, vl′〉 = δ(l, l) (3) To prove this consider the matrix AA ∈ R. Set ul to be the l’th eigenvector of AA . By definition we have that AAul = λlul. Since AA T is positive semidefinite we have λl ≥ 0. Since AA is symmetric we have that ∀ l, l 〈ul, ul′〉 = δ(l, l). Set σl = √ λl and vl = 1 σl Aul. Now we can compute the following: 〈vl, vl...

Statistical and computational techniques for revealing the internal structure that underlies the set of random correlated data exists in a great variety at present; and target decomposition theorems, either in the coherent or incoherent formulation, are well established. In spite of this fact a rather innovative and new concept is presented in this contribution. In turn the Principal Component ...

Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus a...