The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics
technical and used orthogonal transformation to convert a set of observations of possibly …

Principal component analysis has been widely used in computer vision tasks. In image
processing the outliers typically occur within the sample due to pixels that are corrupted by …

Principal component analysis (PCA) is a popular dimension-reduction method to reduce the
complexity and obtain the informative aspects of high-dimensional datasets. When the data …

This chapter introduces principal component analysis (PCA), a technique for dimension
reduction in multivariate datasets. At its core there is a matrix decomposition technique …

Feature Extraction (EF) is considered the effective process among all the data processing
steps of the classification system. In real-life applications, the reliability of a classifier is …

Principal components analysis (PCA) is of great use in representation of multi-dimensional
data sets, often providing a useful compression mechanism. Sometimes, input data sets are …

This article considers a new type of principal component analysis (PCA) that adaptively
reflects the information of data. The ordinary PCA is useful for dimension reduction and …

The principal component analysis (PCA) transformation is a very common and well-studied
data analysis technique that aims to identify some linear trends and simple patterns in a …

Principal Component Analysis (PCA) is the general name for a technique which uses
sophisticated underlying mathematical principles to transforms a number of possibly …

Principal component analysis (PCA) is an essential algorithm in machine learning. It is a
mathematical method for evaluating the principal components of a dataset. The principal …