Importance of Dimensionality Reduction in Image Processing

Abstract

This paper presents a survey on various techniques of compression methods. Linear Discriminant analysis (LDA) is a method used in statistics, pattern recognition and machine learning to find a linear combination of features that classifies an object into two or more classes. This results in a dimensionality reduction before later classification.Principal component analysis (PCA) uses an orthogonal transformation to convert a set of correlated variables into a set of values of linearly uncorrelated variables called principal components. The purpose of the review is to explore the possibility of image compression for multiple images.