Abstract Principal Component Analysis (PCA) is a useful technique for reducing the
dimensionality of datasets for compression or recognition purposes. Many different methods …

Principal component analysis (PCA) is a powerful technique for extracting structure from
possibly high-dimensional data sets, while kernel PCA (KPCA) is the application of PCA in a …

Dimensionality reduction is one of the preprocessing steps in many machine learning
applications and it is used to transform the features into a lower dimension space. Principal …

Data compression is the most important step in many signal processing and pattern
recognition applications. We come across very high dimensional data in such applications …

Principal component analysis (PCA) is a widely used technique for data analysis and
dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solving …

Anomaly detection has become a crucial technology in several application fields, mostly for
network security. The classification challenge of anomaly detection using machine learning …

Principal component analysis (also known as principal components analysis)(PCA) is a
technique from statistics for simplifying a data set. It was developed by Pearson (1901) and …

Large datasets are increasingly common and are often difficult to interpret. Principal
component analysis (PCA) is a technique for reducing the dimensionality of such datasets …

Principal component analysis (PCA) is a commonly applied technique for dimensionality
reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for …

Big databases are increasingly widespread and are therefore hard to understand, in
exploratory biomedicine science, big data in health research is highly exciting because data …