Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between d...

Case study of manufacturing of crystalline drug substance was used to see the application of Principal component analysis. Principal Component Analysis (PCA) is one of the multivariate methods of analysis and has been used widely with large multidimensional data sets. PCA involves a mathematical procedure that transforms a number of possible correlated variables into a smaller number of uncorre...

Principal component analysis (PCA) is one of the most widely used multivariate techniques in statistics. It is commonly used to reduce the dimensionality of data in order to examine its underlying structure and the covariance/correlation structure of a set of variables. While singular value decomposition provides a simple means for identification of the principal components (PCs) for classical ...

Sparse principal component analysis combines the idea of sparsity with principal component analysis (PCA). There are two kinds of sparse PCA; sparse loading PCA (slPCA) which keeps all the variables but zeroes out some of their loadings; and sparse variable PCA (svPCA) which removes whole variables by simultaneously zeroing out all the loadings on some variables. In this paper we propose a mode...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified...