Both principal component analysis (PCA) and linear discriminant analysis (LDA) have long been recognized as tools for feature extraction and data analysis. There has been reports in the open ...

PCA and LDA are two popular techniques for dimensionality reduction that have different goals and assumptions. PCA (un-supervised learning) seeks to find a lower-dimensional representation of the data ...

Demonstrated LDA and PCA using Python. Used a non-image dataset - Wine quality (red or white) dataset. Used KNN classifier and Random Forest Classifier on the dataset. Used KNN classifier and Random ...

These techniques mostly used in face recognition. PCA based on the eigenfaces or they can say reduce dimension by using covariance matrix and LDA based on linear discriminant or scatter matrix.