Survey on Principal Component Analysis - Vikas Babu Gond (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
Large amounts of data are increasing and often difficult to interpret. Principal Component Analysis (PCA) is a technique that reduces the residuals of these data, improving interpretation while reducing data loss. It does this by creating new random variables to keep making a difference. Finding these new variables (keypoints) can be reduced to solving the eigenvalue/eigenvector problem, and the new variables are defined by the data set at hand rather than the values, thus making PCA a modified data analysis technique. In another sense, it is flexible because the technology change is made for different types of products and models. This article will first introduce the basic concept of PCA and discuss what it can and cannot do. Some types and applications of PCA will be described.
Keywords: Correlation Structure; Multivariate Technique; Covariance Structure; Orthogonal Transformation; Principal Component