A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented...

Abstract. a novel method of fault diagnosis for power electronics rectifier based on PCA-SVM is presented in this paper. First, the features of the fault was extracted by principal component analysis (PCA), an SVM algorithm was introduced to train the SVM for diagnosis. Experimental results presented in this paper show that the diagnostic method has very good diagnostic ability and efficient; a...

We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model independent spectral components. We find two significant components and demonstrate that they are qualitatively different from those found in MCG–6-30-15 using the same method. As the variability in NGC 1365 is known to be due to changes in the parameters of a ...

Generalization can be defined quantitatively and can be used to assess the performance of principal component analysis (PCA). The generalizability of PCA depends on the number of principal components retained in the analysis. We provide analytic and test set estimates of generalization. We show how the generalization error can be used to select the number of principal components in two analyses...

We provide a probabilistic and infinitesimal view of how the principal component analysis procedure (PCA) can be generalized to analysis of nonlinear manifold valued data. Starting with the probabilistic PCA interpretation of the Euclidean PCA procedure, we show how PCA can be generalized to manifolds in an intrinsic way that does not resort to linearization of the data space. The underlying pr...

Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is importa...

Facial Expression Recognition is one of the active research area in the field of Human Machine Interaction (HMI) because of its several applications such as human emotion analysis, stress level and lie detection. In this paper, an algorithm for facial expression recognition has been proposed which integrate the Local Binary Patterns (LBP), Gabor filter and Principal Component Analysis (PCA). Th...

This paper considers the development of reduced chemistry models for high enthalpy and plasma flows using Principal Component Analysis (PCA) based methods. Starting from detailed chemistry models, such as multi-temperature and collisional-radiative formulations, a reduction of the variable set (species mass fractions and temperatures) is proposed by projecting the full set on a reduced basis ma...

Global Term Structure Modeling Using Principal Component Analysis Arcady Novosyolov and Daniel Satchkov Abstract Principal Component Analysis (PCA) is a technique commonly applied to the interest rate markets to describe yield curve dynamics in a parsimonious manner. Despite an increase in global investing and the growing interconnectedness of the international markets, PCA has not been widely...