Principal component analysis a multivariate statistical data analysis algorithm widely used
as a dimensionality reduction algorithm in image processing task. In remote sensing data …

Dataset have various number of features. Feature extraction plays a crucial job in
recognition and extraction of most useful data from the dataset. Appropriate mining method …

Principal component analysis (PCA) is one of the most popular feature extraction methods in
pattern recognition and can obtain a set of so-needed projection directions or vectors by …

This paper examines the connection between two-dimensional principal component
analysis (2DPCA) and traditional one-dimensional principal component analysis (PCA) and …

Abstract Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are
commonly used in machine learning. In this paper, we extend PCA and LDA to 2DPCA and …

Principal component analysis (PCA)(also called Karhunen-Loève transform) has been
widely used for dimensionality reduction, denoising, feature selection, subspace detection …

Principal component analysis (PCA) is a popular technique that is useful for dimensionality
reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA …

Information explosion has occurred in most of the sciences and researches due to advances
in data collection and storage capacity in last few decades. Advance datasets with large …

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

Reducing the dimensionality of the data as a pre-processing step of a pattern recognition
application is very important. While applying the well-known Principal Component Analysis …