principal component analysis - OpenGenus IQ: Computing Expertise & Legacy

Principal Component Analysis (PCA) questions [with answers]

Practice multiple choice questions on Principal Component Analysis (PCA) with answers. This is a fundamental technique in Machine Learning applications.

Machine Learning (ML)

Applications of Principal Component Analysis (PCA)

This article gives a brief introduction to PCA and explains the applications of Principal Component Analysis in Neuroscience, Quantitative Finance, Image Compression (with a coding example), Facial Recogntion and others.

Machine Learning (ML)

Whitening with PCA with code demonstration

When we are training our model on images, the raw input is quite redundant because the pixels that are adjacent to each other are highly correlated. The goal of Whitening is to reduce redundancy in these images by making features less correlated to each other and same variance

Machine Learning (ML)

Principal Component Regression (PCR)

Principal Component Regression (PCR) is an algorithm for reducing the multi-collinearity of a dataset. PCR is basically using PCA, and then performing Linear Regression on these new PCs. The key idea of how PCR aims to do this, is to use PCA on the dataset before regression.

Machine Learning (ML)

Kernel Principal Component Analysis (KPCA)

Kernel Principal Component Analysis (KPCA) is a non-linear dimensionality reduction technique. It is an extension of Principal Component Analysis (PCA) - which is a linear dimensionality reduction technique - using kernel methods.

Machine Learning (ML)

Principle of Sammon Mapping

Sammon mapping (also known as Sammon projection) is an algorithm that maps a high dimensional data to lower dimensional data by preserving the structure of inter point distances in the original data. Learn why Sammon Mapping is better than Principal Component Analysis (PCA)

Machine Learning (ML)

Why Principal Component Analysis (PCA) works?

We have demonstrated how and why Principal Component Analysis (PCA) works using the intuition behind the common operations used in the algorithm such as Variance, Covariance, Eigenvectors and Eigenvalues. Eigenvectors represent directions while Eigenvalues represent magnitude the importance

Machine Learning (ML)

Algorithm of Principal Component Analysis (PCA)

The algorithm of Principal Component Analysis (PCA) is based on a few mathematical ideas namely Variance, Convariance, Eigen Vectors and Eigen values. The algorithm is of eight simple steps including preparing the data set, calculating the covariance matrix, eigen vectors and values, new feature set

Machine Learning (ML)

Basic Ideas of Principal component analysis

Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse. We have demonstrated an example of 17 dimensions and given the basic intuition of PCA