The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

Probabilistic Principal Component Analysis is a reformulation of the common multivariate analysis technique known as Principal Component Analysis. It employs a latent variable model framework similar to factor analysis allowing to establish a maximum likelihood solution for the parameters that comprise the model. One of the main assumptions of Probabilistic Principal Component Analysis is that ...

The present paper applied Principal Component Analysis (PCA) for grouping of machines and parts so that the part families can be processed in the cells formed by those associated machines. An incidence matrix with binary entries has been chosen to apply this methodology. After performing the eigenanalysis of the principal component and observing the component loading plot of the principal compo...

This work concerns the principal component analysis applied to the supervision of quality parameters of the flour production line. Our contribution lies in the combined use of the principal component analysis technique and the clustering algorithms in the field of production system diagnosis. This approach allows detecting and locating the system defects, based on the drifts of the product qual...

Wireless capsule endoscopy is the most innovative technology to perceive entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. a discretion way. creates immense pressure onus for clinicians huge number of image frames, which time-consuming makes human oversight errors. Therefore computer-automated system has ...

Case study of manufacturing of crystalline drug substance was used to see the application of Principal component analysis. Principal Component Analysis (PCA) is one of the multivariate methods of analysis and has been used widely with large multidimensional data sets. PCA involves a mathematical procedure that transforms a number of possible correlated variables into a smaller number of uncorre...

Principal component analysis (PCA) has been applied to analyze random fields in various scientific disciplines. However, the explainability of PCA remains elusive unless strong domain-specific knowledge is available. This paper provides a theoretical framework that builds duality between eigenmodes field and eigenstates Schr\"odinger equation. Based on we propose algorithm replace expensive sol...

in the present study, quantitative relationships between molecular structure and anti-tubercular activity of some 5-methyl/trifluoromethoxy-1 h -indole-2,3-dione-3-thiosemicarbazone derivatives were discovered. the detailed application of an efficient linear method and principal component regression (pcr) for the evaluation of quantitative structure activity relationships of the studied compound...