This paper proposes Shared Component Analysis (SCA) as an alternative to Principal (PCA) for the purpose of dimensionality reduction neuroimaging data. The trend towards larger numbers recording sensors, pixels or voxels leads richer data, with finer spatial resolution, but it also inflates cost storage and computation risk overfitting. PCA can be used select a subset orthogonal components that...

Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, intuitive accessible manner, basic principles underlying PCA its applications. Next, present a systematic, though no exclusive, survey some representative works illustrating poten...